spawn start field of antGame in the same spot everytime

# Conflicts:
#	src/main/java/example/DinoSampling.java

.idea/misc.xml

@@ -11,7 +11,7 @@
       </list>
     </option>
   </component>
-  <component name="ProjectRootManager" version="2" languageLevel="JDK_11" default="false" project-jdk-name="11.0.5" project-jdk-type="JavaSDK">
+  <component name="ProjectRootManager" version="2" languageLevel="JDK_11" default="false" project-jdk-name="11.0.3" project-jdk-type="JavaSDK">
     <output url="file://$PROJECT_DIR$/out" />
   </component>
 </project>

epsilonValues.R

@@ -1,26 +0,0 @@
-# Libraries
-library(ggplot2)
-library(matrixStats)
-# file.choose()
-convergence <- read.csv(file.choose(), header=FALSE, row.names=1)
-
-sds <- rowSds(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]))), na.rm=TRUE)
-men <- rowMeans(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]))), na.rm=TRUE)
-print(sds)
-
-# create dummy data
-data <- data.frame(
-  names=rownames(convergence),
-  means=men,
-  sds=sds
-)
-
-ggplot(data) +
-  geom_bar(aes(x=names, y=means), stat="identity", fill="skyblue", alpha=0.7) +
-  geom_errorbar( aes(x=names, ymin=means-sds, ymax=means+sds), width=0.4, colour="orange", alpha=0.9, size=1.3) +
-  geom_text(aes(label=as.integer(means), x =names, y=means), position=position_dodge(width=0.9), vjust=-0.25) +
-  xlab("Epsilon") + ylab("avg. amount of episodes until convergence") 
-
-
-ba <- barplot(names=rownames(convergence), height=men, ylim=c(0, max(men)*1.2), ylab = "avg. episodes until convergence", xlab = "epsilon value")
-text(x = ba, y = men, label = as.integer(men), pos = 3, cex = 0.8, col = "red")

rScripts/avgEpisodesUntilConvergence.R

@@ -0,0 +1,25 @@
+# Libraries
+library(ggplot2)
+library(matrixStats)
+convergence <- read.csv(file.choose(), header=FALSE, row.names=1, skip=1)
+
+sds <- rowSds(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]))), na.rm=TRUE)
+men <- rowMeans(sapply(convergence[,-1], `length<-`, max(lengths(convergence[,-1]))), na.rm=TRUE)
+print(sds)
+
+# create data frame
+data <- data.frame(
+  names=rownames(convergence),
+  means=men,
+  sds=sds
+)
+
+convergence$group <- row.names(convergence)
+convergence.m <- melt(convergence, id.vars = "groups")
+
+ggplot(data) +
+  geom_bar(aes(x=names, y=means, fill=means), stat="identity", colour="black", alpha=0.8) +
+  geom_errorbar( aes(x=names, ymin=means, ymax=means+sds), width=0.4, colour="black", alpha=0.8, size=0.6) +
+ # ylim(0,104000) +
+  xlab("Epsilon") + ylab("Ø Episoden bis Konvergenz") +
+  theme_bw(base_size = 24)

rScripts/avgRewardPerTimestampDiffLRandDiscQ.R

@@ -0,0 +1,56 @@
+# Libraries
+library(ggplot2)
+library(matrixStats)
+ta  <- as.matrix(read.table(file.choose(), sep=",", header = FALSE))
+ta <- t(ta)
+dim(ta)
+head(ta)
+
+# Create data frame
+data <- data.frame(
+  y=ta[,1],
+  y2=ta[,2],
+  y3=ta[,3],
+  y4=ta[,4],
+  y5=ta[,5],
+  y6=ta[,6],
+  y7=ta[,7],
+  y8=ta[,8],
+  y9=ta[,9],
+  y10=ta[,10],
+  y11=ta[,11],
+  y12=ta[,12],
+  y13=ta[,13],
+  y14=ta[,14],
+  y15=ta[,15],
+  x=seq(1, length(ta[,1]))
+)
+ggplot(data, aes(x*1000)) +
+  labs( x ="Zeitstempel", y = "Ø Belohnung pro Zeitstempel", color = "Lernrate") +
+  ylim(-1.5,0.6) +
+  geom_line(aes(y = y, colour = "0.1"), size=1)+
+  geom_line(aes(y = y2, colour = "0.3"), size=1) +
+  geom_line(aes(y = y3, colour = "0.5"), size=1) +
+  geom_line(aes(y = y4, colour = "0.7"), size=1) +
+  geom_line(aes(y = y5, colour = "0.9"), size=1) +
+  theme_bw(base_size = 24)
+
+ggplot(data, aes(x*1000)) +
+  labs(x ="Zeitstempel", y = "Ø Belohnung pro Zeitstempel", color = "Lernrate") +
+  ylim(-1.5,0.6) +
+  geom_line(aes(y = y6, colour = "0.1"), size=1) +
+  geom_line(aes(y = y7, colour = "0.3"), size=1) +
+  geom_line(aes(y = y8, colour = "0.5"), size=1) +
+  geom_line(aes(y = y9, colour = "0.7"), size=1) +
+  geom_line(aes(y = y10, colour = "0.9"), size=1) +
+  theme_bw(base_size = 24)
+
+ggplot(data, aes(x*1000) ) +
+  labs(x ="Zeitstempel", y = "Ø Belohnung pro Zeitstempel", color = "Lernrate") +
+  ylim(-1.5,0.6) +
+  geom_line(aes(y = y11, colour = "0.1"), size=1) +
+  geom_line(aes(y = y12, colour = "0.3"), size=1) +
+  geom_line(aes(y = y13, colour = "0.5"), size=1) +
+  geom_line(aes(y = y14, colour = "0.7"), size=1) +
+  geom_line(aes(y = y15, colour = "0.9"), size=1) +
+  theme_bw(base_size = 24)

rScripts/avgTimestampsPerFood.R

@@ -0,0 +1,34 @@
+# Libraries
+library(ggplot2)
+library(matrixStats)
+ta  <- as.matrix(read.table(file.choose(), sep=",", header = FALSE, skip = 1))
+ta <- t(ta)
+dim(ta)
+head(ta)
+
+# Create data frame
+data <- data.frame(
+  y=ta[1:20,1],
+  y2=ta[1:20,2],
+  y3=ta[1:20,3],
+  y4=ta[1:20,4],
+  y5=ta[1:20,5],
+  y6=ta[1:20,6],
+  y7=ta[1:20,7],
+  y8=ta[1:20,8],
+  x=seq(1, length(ta[1:20,1]))
+)
+ggplot(data, aes(x*1000)) +
+  labs( x ="Gesamtanzahl gesammeltes Futter", y = "Ø Zeitstempel pro Futter (log)", color = "Diskontierungsfaktor") +
+  #scale_y_log10()+
+  geom_hline(yintercept=23, linetype="dashed")+
+  geom_text(aes(0,23,label = "opt", vjust = -1)) +
+  geom_line(aes(y = y, colour = "0.05"), size=1)+
+  geom_line(aes(y = y2, colour = "0.1"), size=1) +
+  geom_line(aes(y = y3, colour = "0.3"), size=1) +
+  geom_line(aes(y = y4, colour = "0.5"), size=1) +
+  geom_line(aes(y = y5, colour = "0.7"), size=1)+
+  geom_line(aes(y = y6, colour = "0.9"), size=1) +
+  geom_line(aes(y = y7, colour = "0.95"), size=1) +
+  geom_line(aes(y = y8, colour = "0.99"), size=1) +
+  theme_bw(base_size = 24)

rScripts/totalTimestampsWhileCollecting.R

@@ -0,0 +1,34 @@
+# Libraries
+library(ggplot2)
+library(matrixStats)
+ta  <- as.matrix(read.table(file.choose(), sep=",", header = FALSE, skip = 1))
+ta <- t(ta)
+dim(ta)
+head(ta)
+
+# Create data frame
+data <- data.frame(
+  y=ta[,1],
+  y2=ta[,2],
+  y3=ta[,3],
+  y4=ta[,4],
+  y5=ta[,5],
+  y6=ta[,6],
+  y7=ta[,7],
+  y8=ta[,8],
+  x=seq(1, length(ta[,1]))
+)
+ggplot(data, aes(x*1000)) +
+  labs( x ="Gesammeltes Futter", y = "Zeitstempel insgesamt", color = "Diskontierungsfaktor") +
+  #geom_hline(yintercept=23, linetype="dashed")+
+  geom_text(aes(20000,40000,label = "opt. Verhalten", vjust = -1)) +
+  geom_line(aes(y = x*1000*23), size=1)+
+  geom_line(aes(y = y, colour = "0.05"), size=1)+
+  geom_line(aes(y = y2, colour = "0.1"), size=1) +
+  geom_line(aes(y = y3, colour = "0.3"), size=1) +
+  geom_line(aes(y = y4, colour = "0.5"), size=1) +
+  geom_line(aes(y = y5, colour = "0.7"), size=1)+
+  geom_line(aes(y = y6, colour = "0.9"), size=1) +
+  geom_line(aes(y = y7, colour = "0.95"), size=1) +
+  geom_line(aes(y = y8, colour = "0.99"), size=1) +
+  theme_bw(base_size = 24)

rawResults/antAnalysisAvgReward.txt

@@ -0,0 +1,15 @@
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rawResults/avgTimestampsPerFoodDiffDiscounts.txt

@@ -0,0 +1,9 @@
+# Average Timestamps needed to collect one food piece, mean over 1000 collected food; Discount factor for rows: 0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.99
+103.857,48.501,49.283,44.149,44.51,42.402,45.102,41.896,43.104,40.76,41.0,39.134,46.756,44.282,45.834,44.942,41.078,43.66,42.812,41.672,44.022,41.196,42.106,41.384,42.174,42.736,45.792,43.482,48.132,44.14
+86.807,32.319,29.317,26.51,25.664,24.348,24.994,23.972,24.46,24.252,24.128,23.758,25.184,24.872,25.286,24.356,24.322,24.262,24.26,24.446,24.888,24.302,24.866,24.236,24.548,24.866,25.894,24.872,26.212,24.632
+81.071,30.951,26.07,24.804,23.196,22.67,23.374,22.388,22.666,22.776,22.432,22.294,23.318,23.018,23.44,22.97,22.502,22.776,22.692,22.382,23.178,22.932,23.098,22.726,22.584,23.062,23.548,22.824,23.672,22.73
+92.437,29.962,26.044,24.465,23.196,22.67,23.374,22.388,22.666,22.776,22.432,22.294,23.318,23.018,23.44,22.97,22.502,22.776,22.692,22.382,23.178,22.932,23.098,22.726,22.584,23.062,23.548,22.824,23.672,22.73
+120.382,29.993,26.154,24.518,23.196,22.67,23.374,22.388,22.666,22.776,22.432,22.294,23.318,23.018,23.44,22.97,22.502,22.776,22.692,22.382,23.178,22.932,23.098,22.726,22.584,23.062,23.548,22.824,23.672,22.73
+281.912,37.184,28.825,26.807,23.42,23.018,23.428,22.431,22.718,22.802,22.444,22.352,23.414,23.031,23.448,22.97,22.508,22.782,22.698,22.391,23.184,22.936,23.102,22.736,22.584,23.062,23.55,22.835,23.674,22.738
+536.915,54.205,42.919,33.225,26.309,23.071,23.713,22.6,22.675,22.81,22.478,22.313,23.675,23.25,23.601,23.064,22.599,22.866,23.312,22.591,23.195,23.55,23.354,22.735,22.604,23.066,23.549,22.854,23.698,22.76
+2205.811,288.101,186.619,128.568,108.486,53.722,67.856,58.127,53.659,39.438,44.677,28.751,54.808,33.351,26.386,25.356,24.812,25.128,25.056,24.712,25.626,25.316,25.552,25.112,24.874,25.446,26.05,25.17,26.16,25.058

rawResults/convergencSimpleQLearning099disc09LR.txt

@@ -0,0 +1,21 @@
+#same speed and distance. distance and inJump. Q LEARNING; distance and inJump
+0.05,40,41,35,36,36,40,37,36,40,38,33,36,33,29,39,39,37,44,36,38,39,35,37,39,35,40,39,36,45,33,41,38,46,33,40,40,40,42,39,41,43,40,42,35,33,36,39,35,38,37,42,33,37,46,38,36,49,38,36,37,41,42,40,38,40,38,35,40,38,38,46,37,43,49,35,42,37,42,49,35,39,39,35,38,37,37,38,42,37,39,37,40,45,41,38,61,40,39,41,43
+0.1,42,36,48,37,43,38,39,41,39,38,36,38,43,35,39,38,38,39,42,41,39,44,40,44,43,39,39,39,50,34,41,36,37,34,46,39,42,41,38,51,47,37,39,44,36,45,40,40,40,36,38,32,38,38,42,41,50,38,39,39,40,40,38,41,46,43,37,41,32,39,39,43,43,48,40,39,40,43,50,38,41,38,39,32,40,44,40,39,40,35,36,41,37,41,41,36,45,41,40,48
+0.15,42,40,37,47,45,36,41,35,43,43,42,41,40,36,41,39,38,44,44,40,40,39,44,45,41,36,39,38,45,38,36,36,40,39,44,41,44,47,35,53,51,42,39,39,46,40,42,41,41,36,37,40,41,47,40,43,35,36,40,39,41,44,45,47,42,53,36,51,40,41,39,40,46,41,37,44,49,38,42,45,39,42,39,40,39,43,42,41,43,39,39,41,46,38,43,39,48,79,41,48
+0.2,44,44,52,45,42,43,40,43,44,37,36,41,45,36,38,43,41,41,45,41,37,37,49,42,42,40,40,50,41,44,38,41,45,37,41,55,56,38,43,43,46,46,44,38,43,38,60,44,45,38,39,40,47,53,40,40,38,43,44,39,41,42,47,41,42,46,37,42,41,47,41,55,53,41,41,41,42,44,39,39,38,47,36,41,39,45,52,37,36,42,44,46,44,39,51,41,46,46,40,49
+0.25,48,43,38,51,36,47,44,52,44,47,44,46,40,41,50,44,50,44,48,44,50,43,50,40,41,41,50,41,43,52,44,46,43,41,50,40,47,45,39,45,52,38,45,41,44,46,51,51,46,44,65,39,41,52,41,39,48,41,39,51,65,49,43,41,42,52,42,37,51,38,40,42,46,50,41,42,53,39,44,38,48,49,43,42,46,46,43,46,39,35,50,51,45,44,49,48,46,50,38,51
+0.3,46,39,44,65,45,50,48,42,43,39,48,42,48,44,49,63,44,45,42,52,55,46,39,50,48,50,48,69,50,43,36,37,49,45,46,43,46,46,53,51,57,43,50,40,54,53,50,65,68,48,38,49,50,43,50,43,55,39,41,40,51,49,45,54,59,49,53,43,44,50,37,45,48,44,48,44,45,41,49,39,46,48,45,41,51,43,55,55,44,37,42,48,40,38,63,45,46,56,50,51
+0.35,52,60,67,48,52,47,45,49,42,51,44,39,58,49,52,59,47,52,44,63,41,80,41,41,47,46,58,56,63,62,41,47,50,49,42,51,48,57,49,53,45,45,54,41,43,52,50,45,45,50,63,34,38,50,52,37,52,53,43,50,74,45,61,53,49,56,46,51,49,62,38,45,44,47,45,54,45,66,52,60,54,44,47,52,51,66,94,51,44,43,52,44,49,43,65,65,54,79,53,44
+0.40,48,44,53,51,51,59,46,84,53,58,48,46,56,44,52,42,58,44,42,57,57,49,42,61,63,52,53,42,52,55,45,47,54,54,47,41,56,56,50,51,42,56,45,48,49,66,46,56,47,44,51,36,43,48,48,81,41,52,48,43,41,71,50,51,52,58,52,42,49,54,48,45,47,40,54,47,55,52,43,46,67,50,48,44,65,62,49,67,50,43,72,49,43,51,48,55,55,45,60,54
+0.45,74,57,57,67,45,39,57,70,61,49,41,54,46,50,50,51,65,48,45,68,70,40,42,45,56,53,68,47,106,60,51,78,52,52,46,43,62,60,52,51,49,48,49,54,46,45,48,50,52,45,48,47,53,44,51,77,40,77,45,61,50,44,50,50,55,56,44,51,47,41,45,36,47,41,80,58,63,64,61,58,46,61,54,52,59,56,46,52,56,37,50,48,45,60,49,44,60,54,53,53
+0.50,68,77,84,53,102,41,49,59,69,65,45,40,59,69,57,49,46,52,45,64,77,40,42,51,48,72,59,55,67,42,85,53,49,72,70,70,50,86,48,53,58,54,52,39,58,42,47,55,70,49,46,67,57,45,67,58,51,72,59,46,49,46,72,56,71,58,77,56,55,57,52,50,37,85,62,57,59,82,61,53,59,53,70,53,64,55,67,50,46,37,48,64,53,43,50,47,56,63,65,60
+0.55,55,54,51,61,55,48,61,58,76,79,46,68,58,87,59,42,40,102,60,62,78,40,44,86,56,61,71,68,59,46,65,60,56,45,71,57,54,64,78,64,116,59,61,58,76,45,50,74,79,62,50,51,46,63,57,56,51,62,57,48,51,47,51,63,61,67,67,48,55,52,79,44,42,50,59,52,56,69,51,53,65,65,54,68,69,62,66,48,64,66,80,68,51,47,53,61,63,58,52,55
+0.60,64,65,62,60,56,59,77,113,82,97,45,49,62,79,56,57,43,65,53,70,77,57,44,67,69,71,57,77,60,80,68,69,55,72,54,51,61,59,88,77,66,56,73,54,82,57,60,52,54,81,65,49,59,87,96,54,87,60,57,53,47,47,52,56,57,52,89,86,63,80,63,93,44,92,61,83,96,51,62,90,151,51,50,71,80,88,84,59,92,53,45,61,65,62,59,48,63,95,142,53
+0.65,65,80,95,71,49,56,60,154,81,61,39,53,53,71,78,58,48,79,51,55,59,64,86,74,143,66,68,100,73,60,41,49,80,68,51,54,70,54,80,82,200,60,58,51,59,67,59,69,53,71,65,47,78,53,93,76,87,64,70,54,63,57,70,62,64,75,52,62,53,53,57,46,50,84,56,57,87,92,72,63,99,40,74,83,68,54,69,65,52,55,49,75,97,103,51,56,101,74,65,55
+0.70,104,85,65,113,78,66,61,89,62,126,64,119,54,85,49,77,54,110,66,120,78,192,56,52,67,64,154,120,94,86,41,152,57,55,53,62,68,63,63,120,57,127,68,54,52,86,61,55,71,52,97,70,58,66,56,62,75,79,215,80,71,50,57,56,49,63,103,87,82,70,63,55,58,67,126,57,91,71,74,73,123,60,57,69,61,61,57,76,57,59,49,58,84,58,111,102,56,91,85,62
+0.75,67,74,65,93,78,108,106,63,70,195,67,90,107,80,77,103,107,103,80,115,111,119,60,59,130,77,186,81,69,97,62,90,64,88,123,79,55,93,82,57,56,93,67,68,76,49,54,52,50,102,58,66,169,61,68,46,71,69,88,57,98,51,65,156,72,59,71,90,55,81,63,62,98,116,72,81,107,47,71,212,82,69,65,117,71,54,105,74,86,109,127,92,81,112,54,70,77,75,68,56
+0.80,99,192,79,67,106,137,113,66,83,67,60,117,279,71,72,73,62,105,108,88,123,93,51,71,178,93,83,91,82,122,81,149,143,96,92,151,58,58,106,133,56,115,102,88,72,75,69,82,82,125,96,68,110,120,83,45,86,74,87,126,101,49,58,89,105,76,92,95,88,110,76,111,77,125,72,86,99,70,64,130,114,82,90,82,304,72,97,216,82,104,115,74,78,107,74,54,51,103,153,56
+0.85,252,96,99,77,64,72,86,65,71,205,161,123,117,196,60,128,102,97,66,350,70,237,206,79,71,97,86,91,75,183,71,91,141,97,132,80,99,58,254,72,289,465,83,317,55,157,103,108,90,204,114,120,106,148,174,115,188,153,67,132,95,122,168,81,99,88,153,123,136,97,137,56,66,50,84,266,69,58,84,73,87,55,72,112,61,66,121,79,96,132,106,67,76,55,163,77,68,89,174,71
+0.90,74,82,122,124,112,84,145,70,76,93,114,61,124,78,101,131,143,74,85,216,70,77,162,56,122,87,85,86,75,100,77,208,223,121,494,208,168,142,294,110,74,74,233,102,51,269,191,110,133,149,68,78,52,84,172,102,179,106,65,148,88,81,73,140,76,181,307,124,60,147,123,179,187,50,260,151,138,67,160,73,130,68,246,141,88,93,69,227,110,99,78,191,165,52,130,159,119,65,101,104
+0.95,160,94,144,103,100,181,408,184,300,117,183,342,75,58,189,152,142,157,141,188,242,131,84,353,159,223,86,86,135,62,185,121,60,72,238,434,113,150,97,117,335,100,174,93,74,308,338,87,74,127,68,159,295,165,141,227,108,258,212,83,130,387,259,117,447,357,150,56,94,145,129,105,222,486,114,106,283,58,139,126,65,121,76,141,89,72,163,81,97,156,186,325,76,78,81,138,94,147,220,71
+1.00,176,314,95,92,451,125,100,72,235,154,188,103,95,137,113,166,108,236,52,115,151,282,81,513,339,102,203,147,474,311,199,152,190,316,271,443,211,172,174,62,314,233,196,185,236,213,263,300,132,150,327,128,223,176,120,290,190,125,119,148,349,263,125,358,80,194,258,56,71,100,112,250,514,51,235,125,389,178,919,686,65,274,245,174,202,164,381,82,186,111,174,356,81,72,164,187,128,137,183,116

rawResults/convergenceAdvancedMC.txt

@@ -1,4 +1,4 @@
-# 4 speeds, 4 distance. Distance, inJump and incomming speed; Monte Carlo
+# 4 speeds, 4 distance. Distance, inJump and incoming speed; Monte Carlo
 0.05,8565,3803,4010,30744,5419,1733,30335,4070,7502,7880,12776,2242,10489,4384,23578,14718,17801,7770,4097,5905,4027,5969,8492,1994,1542,18577,2346,13550,2279,5072,39861,15109,4419,10970,6150,2359,3093,7131,5932,18531,6887,22726,4925,3320,6832,13704,10372,25447,22945,28991,4526,12461,1831,20964,21963,2749,7112,3260,33941,14907,9168,7133,6590,21075,5302,2546,10164,3101,3204,7850,40776,38750,32898,14204,4431,12038,13430,13976,7385,7647,9634,12230,6635,9532,4181,12857,10473,2609,3827,4800,4316,5114,4153,6282,4737,5078,7358,11660,4750,16256
 0.1,1550,4416,3473,2821,2101,3635,5168,9641,7074,5623,2571,2253,2595,4347,24575,4239,4552,6372,6638,2686,33218,1976,7638,2479,3555,4496,2321,11712,1934,3668,9398,1312,4422,3217,9472,7053,5539,2140,2928,1576,12121,5023,2458,5947,2374,10197,1701,6186,7013,3061,3108,1856,4052,2327,2287,2026,1606,1567,2414,3756,2648,3409,4195,4803,11940,2171,1574,2656,3221,2044,2250,1528,1348,7785,3141,6622,25696,4520,2118,2489,7125,4182,1495,2475,5037,3839,4218,1929,2302,5339,1827,5444,3710,47322,2555,2373,2108,1550,4416,3473
 0.15,2328,3189,5422,1943,2057,1630,1405,2079,8071,22741,4056,1704,2727,2339,10381,1246,1831,6658,2760,9416,4036,1243,3716,1199,8678,5480,4038,42601,1133,3576,3852,4014,1487,5849,4275,3766,2240,3917,2164,3695,1954,1869,1854,1751,2785,2334,2821,5942,1310,1680,7867,1987,2345,1785,3898,1598,4382,2881,4874,2345,2483,3962,8058,10204,6444,2885,8494,5135,7150,1041,8020,24960,1720,45430,3546,3751,2077,2269,6051,5372,4247,10739,7114,1169,1757,1162,2699,1765,1367,3226,2290,3695,1756,2452,7385,2599,1426,7600,15285,4712

rawResults/convergenceAdvancedMCnegRew.txt

@@ -0,0 +1,21 @@
+# neg Reward when jumping
+0.05,13127,10042,8569,14055,23244,2675,6334,4085,10753,23753,18247,2950,13264,16181,4512,16393,2628,25992,16763,13006,3791,6693,3181,2434,7714,2807,5194,9244,43228,6497,14502,5554,10468,13732,2787,149024,3660,15067,39860,5654,1452,8265,26522,6075,1564,46233,1346,4651,3832,1797,2986,1960,4018,3878,11424,6712,5565,4119,4927,49845,14464,15068,5122,5017,14182,13186,2543,11122,3879,1692,12035,17349,4686,9228,1689,11068,13202,7352,27103,3823,5354,6412,13770,4099,10944,2312,3314,5555,27211,1875,4148,5640,4478,9710,7405,87247,4725,35525,1702,2990
+0.1,14335,2615,6201,5676,17701,15942,6057,3164,8345,2425,1983,1800,6547,8996,845,1986,2132,2845,2633,5693,3551,4790,6061,3850,8209,2210,2455,2885,3010,5133,7906,1669,2550,5285,14340,2402,3750,2155,13988,3274,929,1882,15921,1846,12900,3317,2602,6598,8876,4061,4360,5528,4341,1246,2360,2313,1086,6906,4982,6967,1928,2298,3243,4868,7458,1618,5170,18097,5497,6586,2421,2409,2578,4735,8179,2574,4037,6341,4452,3115,7323,2249,2767,2528,2489,5955,1477,2537,7012,3087,4709,5563,4546,2397,5348,3776,7168,2183,4619,4336
+0.15,3949,2473,33930,1588,4565,1853,3669,2191,5687,7433,1799,1640,2317,12798,6942,4328,10910,1444,4726,5030,1979,2452,994,3531,2127,2306,7729,3683,2684,1542,3619,1849,1353,1946,2287,1205,2257,2230,2202,5574,3363,1339,4094,1827,1223,1301,1468,5430,1365,2478,2540,4063,2614,1173,5777,1936,13638,1636,1913,4127,1168,2483,2567,1867,3508,1498,1735,2114,3723,2625,2203,3336,1780,1421,1480,4786,6579,2350,3523,4576,1070,2246,2210,6894,2561,9787,1831,6486,2021,19097,1716,1816,1559,3191,3360,1801,1908,4191,2811,1811
+0.2,3268,955,1021,3864,1977,2116,1396,1604,1714,2655,3365,2809,1174,1612,2134,2353,1877,4972,1872,1323,3616,5693,3807,1866,3426,3924,3523,1855,6889,2411,2205,1154,2950,1749,4014,1353,26045,1346,3223,8364,1575,7367,2518,2603,3094,1561,2686,1891,1539,1970,664,1888,1564,3000,1885,3544,2112,2542,2975,1643,1186,1398,1797,2347,1726,3386,1969,4046,1980,5238,4035,6871,5566,1916,4024,2434,1555,2536,942,4291,2909,1780,1875,1605,2001,1119,2105,3149,2597,885,2187,3248,1074,2594,2000,1789,1376,2366,1891,1146
+0.25,3510,3792,2524,2155,2012,2793,1816,1933,2192,2583,963,1218,1488,1280,855,2961,3031,1204,1379,1329,2309,1935,4170,4359,4827,1943,1762,1473,1706,3708,3116,3076,6117,2971,2147,6767,5148,2073,3105,1257,1392,1709,4553,1544,753,3107,1712,1753,1868,1677,2145,4900,3527,3073,1720,2189,2109,2379,1782,2361,4636,4831,1222,1625,1905,1127,1423,1804,2714,2037,1656,1282,1746,2291,4651,2335,3106,1769,5828,1843,1884,29521,1863,3096,2670,1976,2725,1835,1029,976,1613,2005,3839,1731,1866,1532,1871,856,1610,4777
+0.3,4791,2872,1999,1337,2176,1168,2137,3453,1192,1166,1791,1367,1222,2499,1912,2522,1566,2500,2110,10074,1474,5624,1487,3748,1307,1511,2794,1838,1322,1502,1585,847,932,706,3204,1375,750,2576,2935,698,1688,1756,3598,1778,3295,2903,3745,2008,2654,2709,1852,1500,3169,1108,1175,3677,2398,3132,1675,1723,2881,3735,1999,1722,1283,2185,2176,2971,1814,8661,2084,1519,6287,1637,4214,2251,5766,2800,1733,1892,946,3759,1340,3341,1477,1104,990,1978,2102,1301,1900,4858,1442,1363,1291,892,1420,1399,1443,2160
+0.35,3144,1978,1367,899,2047,1578,2028,1543,1653,2175,3898,3124,2301,2365,1135,1834,1484,1758,1955,1726,2844,1813,1941,1506,3552,1611,1579,1573,2354,2143,6142,1866,1647,1875,1362,733,2561,2615,4592,2449,2273,1596,1358,1703,1025,1472,2458,5316,1328,2011,3856,1951,2118,1219,887,960,2292,1922,1963,2097,1544,1289,848,1301,1622,5232,2031,1611,3139,2170,2030,2562,3012,1647,3956,1921,1754,1584,2393,1950,964,1763,1073,2608,1207,1837,1593,3945,1522,2871,1491,2116,2603,2635,2258,3129,5708,1984,3942,1673
+0.40,1747,530,4366,2330,2845,4726,4799,2249,2398,3699,1973,1381,1466,722,3054,2660,608,1739,4073,778,1837,1136,2644,1348,2324,2379,2433,2253,1680,2331,1439,2864,1976,4342,1999,2988,3889,1562,2743,1766,1611,1233,1146,3178,1590,1556,2508,1564,1576,3302,2354,1842,2549,1296,3109,2289,2180,1590,2228,5308,1050,2687,3535,1454,2571,2087,6168,1759,3095,2296,1942,2443,2294,1027,1155,1751,3166,1546,4627,9175,1147,1412,3809,1428,3425,2626,1957,11729,1638,1830,2176,5053,2817,905,4809,1735,1772,2513,1495,878
+0.45,2810,982,1796,2872,2048,5078,9382,1779,6345,4061,3810,809,2424,4970,1539,2209,1593,2090,969,2687,1413,2471,2762,3114,1615,3034,15334,1842,1591,1569,2800,2114,2807,2979,1907,2440,1982,5081,2863,3121,2964,1704,1543,2840,1133,2931,3777,3909,3245,1478,2847,1476,5783,1487,2397,3976,4268,1478,2382,1333,1696,1533,2052,2634,1485,7021,2734,1701,1578,2533,1807,2909,2752,2359,2704,4948,2087,4209,2312,5492,1268,1518,1369,4738,3006,2807,1966,1237,4877,3013,765,1026,2005,3735,3017,1151,3733,1383,1813,1116
+0.50,3780,4905,1508,1515,2847,2723,1374,3111,1739,3479,1426,1405,1541,4273,2461,2250,2603,1077,2778,4003,9784,1986,4932,699,1840,4356,1852,1521,1183,2015,3966,1677,3418,1708,1570,3479,2591,1581,2792,1381,3990,17514,1725,2257,3287,3386,2467,4252,2669,1678,5490,5032,1523,5681,2617,1431,1976,2362,2959,3807,2526,2464,2749,2085,2068,1601,3492,4168,2805,1000,2374,2833,1342,3734,7337,2505,2968,3120,1976,2095,6447,4490,2580,3905,1471,1557,1265,3747,1698,2580,2332,2407,1086,1992,2072,3305,1466,5027,2241,1740
+0.55,4894,2020,2725,1443,2388,1549,1599,1608,1156,2004,2340,4378,1651,1331,6220,3843,2112,2609,1622,1754,2877,2925,3040,2034,4180,2373,10642,4244,1971,1864,1389,1339,8562,3260,2747,2470,2124,2377,6522,2875,2752,1521,2071,3905,3162,5397,2542,2175,2460,4040,1963,2045,4940,3473,2904,1104,2199,1551,24920,1235,2928,2013,2520,3446,2197,1092,5901,3040,1728,7339,2280,2921,1040,3021,1893,2550,4336,1588,2813,7869,1413,928,960,2060,2232,2217,2028,1917,1334,5005,4776,2393,3896,2837,4675,3179,4820,2413,1783,1475
+0.60,2907,3251,7965,4412,3716,2183,4838,6754,9812,4002,2615,2011,4358,3573,6479,2614,2143,3239,1926,2475,2446,2764,2082,4686,3437,2410,5223,3869,1624,3488,9126,1560,8726,1239,1195,1484,1404,2357,5689,5385,2143,3069,3188,3675,2972,5688,2491,4889,3878,4644,2857,1983,4232,2343,11402,3019,2552,3481,2799,5638,3724,3893,2104,3939,3035,5676,3795,1219,2207,1572,1408,2615,4317,2444,1901,34366,1848,2068,8192,3351,3083,4918,7179,3186,2322,6968,7844,3021,2353,6283,12977,6083,2636,1349,1619,3398,5822,2608,5273,2006
+0.65,9232,7810,9842,3573,4081,2854,1585,4202,3114,4601,1914,7769,6260,10866,5719,2036,2705,2787,40999,11577,2514,2749,1702,5091,12420,4277,6985,9756,3112,8263,5479,1837,8647,4610,7619,6085,3821,2890,2428,2412,4381,3745,3478,2637,12475,2162,2704,10316,5697,2883,6426,16244,7461,4625,1632,3541,4127,11160,11520,3228,2286,3437,8028,3491,3753,17455,2292,7702,6814,2601,11101,4898,6817,11753,13658,3484,2070,2757,3058,10921,5115,2572,2763,4521,3174,5989,19466,2038,2588,4797,2181,2712,29279,3599,5079,2441,1599,8493,2961,3251
+0.70,2685,2467,3550,2006,6238,7136,15201,3491,10225,1887,12895,6072,5705,5325,4296,2624,7016,4100,5419,1775,9536,3446,1578,1709,2720,7435,3004,3822,4361,4461,1454,2626,2940,3159,8150,9754,3449,6145,6220,5584,16839,1754,23858,5849,6052,28776,2987,27742,3308,7494,2931,8790,2642,2343,7788,4329,2840,11974,3188,3075,15182,7683,5173,2654,5370,6937,14128,2506,3138,18603,9750,4276,5302,3311,3734,8920,9798,4361,15380,4293,13006,2358,9189,7454,3753,11558,12671,4079,1760,6234,4786,1578,7864,6593,3687,4071,4732,8769,2063,11293
+0.75,2435,1810,2927,5853,8581,2919,4972,13850,27659,3676,5045,15290,20998,6168,12611,6457,5973,7011,4417,3962,10505,4501,6400,13663,2911,5571,3426,35278,5720,1583,7418,4238,6759,6055,5948,2957,4696,15687,10830,5466,2518,16600,6652,11823,14720,14016,1435,2422,14354,8595,3711,4850,7099,5327,4409,19306,9417,756,5046,3027,5068,9536,3475,5082,3899,4261,10022,4502,2673,8239,4705,6827,1496,2552,21517,4950,3436,9990,2318,2670,27394,4838,25057,3800,9348,3824,1908,20923,5543,2900,11697,5162,7402,3192,14241,5040,2346,11160,9846,6727
+0.80,23371,13662,2610,3123,26600,5906,23183,11485,2044,15258,56366,4336,13184,16712,13557,11630,11685,4179,6475,22117,3277,21088,5519,4128,5046,7032,7460,5568,3386,8238,4008,3251,19140,4465,11027,7443,9430,8304,15718,5129,9187,7542,10458,3791,32996,10154,18294,2742,7202,14167,10913,14566,6472,3560,2730,7425,4317,6402,17221,24562,3051,13622,41040,7260,24759,2382,6860,3922,6252,4609,5775,11406,3102,10563,14923,5731,3119,8198,19199,11265,5756,10868,8093,5442,7096,16954,5440,5237,8315,10868,19020,3271,9158,23881,12818,17414,24735,13355,16241,8234
+0.85,5692,4946,1847,10285,14826,70294,7646,9324,11638,7958,50723,11420,3453,5384,9487,29705,30433,4195,13090,27090,20988,10246,13323,4886,4881,4299,4309,6680,5921,27922,10940,16090,20098,39213,77251,59600,7327,12890,8958,48774,26761,10285,37887,23265,3874,15325,21764,5062,20689,20026,19202,5980,17445,6953,13882,16977,58621,5112,8648,42385,12739,7147,8578,4804,13216,4628,5658,7131,3338,11382,13816,57924,8512,19509,8975,14587,6045,31106,60578,6903,7097,14691,22969,6060,10760,12224,9350,24530,5787,9872,4687,7573,8933,61880,11642,15850,19393,18662,20337,5487
+0.90,18330,19318,8805,23257,11711,33491,21779,18722,36910,26080,5882,10829,8848,9255,3258,32657,16622,9681,9476,6754,5958,34068,6549,16193,9290,53356,36944,15579,5563,8232,8482,6581,11648,8438,12394,17378,16809,17818,5688,31617,25372,18421,29680,26465,9368,17077,24886,7977,5025,45452,36999,17392,24618,10553,31856,84419,36957,47410,10986,42540,13596,27220,12345,22884,6044,45135,10521,22076,9204,8594,29542,26473,5245,10892,17621,15232,17679,5220,9361,20508,25484,14263,9908,89045,10082,8319,13466,10045,50753,30676,14909,52776,20422,65610,10554,11853,7408,11605,36720,23032
+0.95,24269,14543,6830,3800,41079,4807,26456,36057,12483,8749,47279,27177,17286,9802,7115,3756,85275,14507,34993,15139,32066,1275,3794,29286,15184,90742,27554,23713,6453,15157,7227,8048,47550,84540,93729,68601,6275,4713,30578,5084,94239,7315,8193,46871,96466,3764,70915,62947,32258,66228,2133,5084,12686,62905,19158,20940,36270,9037,34034,15016,15530,46276,11063,8586,15635,7196
+1.0,11364,6363,8012,19730,8425,21388,7864,18427,34072,98639,52381,35105,86487,73913,88033,76264,30103,9522,31049,3182,6600,4078,39236,59239,22968,21540,98395,4052,5612,5001,9933,83766,88344,75849,59554,38619,20400,3709,14813,92207,14994,87801,1645,70868,79790,1587,23322,22071,13903,3589,9722

rawResults/convergenceSimpleMC.txt

@@ -0,0 +1,21 @@
+# Same speed an distance; distance and inJump; Monte Carlo
+0.05,3618,75,1959,674,653,1038,2851,96,774,820,83,726,2589,1424,171,99,29,484,32,2075,1906,2276,3147,94,1798,60,393,272,88,104,2763,1052,15,2521,1633,91,481,1029,3664,360,201,255,2775,496,84,15,6007,423,9,125,242,1393,80,1705,305,109,1492,215,161,102,1220,228,1157,463,1361,132,381,190,2388,4683,4670,313,140,3836,2942,1630,117,7794,26,303,291,1616,3593,418,240,44,364,17,241,2622,359,2833,278,5275,1481,1776,482,1513,567,189
+0.1,333,2264,357,322,3972,270,844,546,52,838,1245,911,174,916,34,1053,944,921,68,1138,83,1652,303,638,334,406,476,185,298,213,1666,845,15,212,369,5,463,195,2294,550,155,249,2875,64,219,15,569,120,9,535,43,41,2609,2955,6,875,848,114,434,77,630,3697,33,3,897,500,343,23,117,1844,129,54,39,101,531,156,109,77,1420,21,1137,48,15,161,725,1589,44,317,2383,2432,557,140,816,482,1493,74,1008,331,210,74
+0.15,324,460,1804,322,918,155,401,2090,52,62,23,377,98,397,35,112,41,901,32,396,283,111,46,638,2008,96,729,1521,79,354,67,54,131,46,482,5,636,9,474,64,226,56,28,64,1812,54,125,400,1147,40,268,65,744,158,6,105,45,77,267,271,207,364,760,3,370,734,60,31,14,115,80,39,39,443,133,70,748,27,321,68,720,135,210,153,1101,228,94,149,2324,167,1332,114,219,212,891,195,13,41,475,74
+0.2,1929,64,352,105,1092,2078,81,182,52,358,23,603,111,405,160,55,170,513,77,469,65,50,33,103,588,90,121,54,1462,337,77,388,100,96,257,5,99,9,1093,64,207,136,89,15,57,15,76,61,278,57,183,101,1792,334,6,875,26,98,171,285,201,421,760,3,403,355,316,24,14,886,549,17,21,1102,1125,15,784,19,278,59,166,659,610,336,237,109,266,91,73,209,406,123,298,316,354,43,13,1066,1887,417
+0.25,590,50,302,223,160,91,83,182,52,387,23,341,95,192,160,52,144,672,114,453,65,100,33,103,83,9,279,142,107,192,546,165,50,63,253,5,99,9,242,142,264,134,89,15,32,15,182,61,610,39,183,50,2132,358,6,875,26,156,400,161,152,190,190,3,383,355,25,24,40,633,32,17,34,52,806,15,88,33,53,114,953,1426,523,10,229,205,284,121,118,145,204,106,359,632,592,60,18,239,2,407
+0.3,195,50,176,75,175,65,909,182,58,211,23,637,859,132,160,52,28,469,24,1094,65,523,54,103,93,607,278,985,65,224,947,370,62,36,253,5,99,9,345,142,166,134,89,15,47,1057,189,624,129,52,183,130,623,343,6,96,26,98,399,136,98,139,109,3,265,412,126,59,33,549,386,24,124,42,685,136,39,26,55,52,74,663,9,96,211,205,206,121,7,1099,482,1482,371,218,125,61,598,737,2,407
+0.35,192,50,21,87,166,102,153,182,381,251,184,67,324,169,86,52,28,16,24,375,65,82,347,159,496,44,264,137,125,48,7,311,124,36,212,5,99,9,311,42,113,55,87,15,32,163,252,128,205,52,60,126,93,532,6,96,15,95,348,248,98,152,105,3,287,193,503,36,75,472,378,10,39,99,1127,127,39,34,130,78,74,263,9,96,85,226,56,26,7,762,34,56,56,511,119,83,160,247,2,130
+0.40,135,26,52,322,108,130,321,257,166,349,473,59,12,553,125,52,28,125,151,101,98,69,59,215,98,50,84,143,261,279,7,311,395,44,212,30,99,9,127,48,70,55,118,37,86,95,202,674,624,109,85,238,152,97,6,96,14,64,348,111,98,118,44,3,287,60,503,25,76,123,107,10,241,42,1127,112,72,27,6,78,76,33,820,10,163,85,164,379,7,198,69,168,58,166,72,102,616,135,2,150
+0.45,143,50,12,48,156,130,140,240,204,339,112,45,12,56,125,52,32,131,186,158,58,69,8,141,126,76,78,28,33,337,195,164,139,101,212,21,99,9,232,48,138,26,89,107,284,120,81,668,722,12,60,58,76,33,6,109,17,128,106,111,98,243,80,74,395,44,456,25,102,123,107,63,221,40,59,98,61,27,30,74,304,28,360,10,42,85,191,379,7,198,56,156,156,43,115,82,13,177,2,145
+0.50,127,48,43,48,156,48,403,167,71,116,61,51,12,196,56,52,32,31,26,92,9,223,9,60,102,9,88,90,33,337,195,112,22,101,212,18,186,9,302,49,124,56,57,107,144,127,115,46,520,12,60,135,76,18,6,157,95,112,301,109,98,136,84,100,8,124,277,36,476,143,408,97,157,40,231,71,61,51,116,97,158,74,490,10,75,85,189,248,7,126,56,281,101,252,8,89,13,228,2,145
+0.55,112,19,33,48,64,73,136,92,71,141,122,37,12,82,23,301,253,31,26,36,76,99,80,60,319,9,88,27,36,177,45,89,210,51,212,10,185,9,84,14,140,375,69,11,148,83,81,79,520,89,60,116,82,118,6,153,106,194,329,738,98,47,89,119,8,129,4,40,205,255,277,200,96,81,77,23,92,90,29,28,217,24,165,10,27,119,189,248,7,76,106,41,58,67,8,89,75,23,2,116
+0.60,43,96,38,243,25,84,219,92,242,95,47,37,12,335,23,147,7,31,26,38,100,109,117,60,337,260,80,16,98,79,62,84,15,51,262,10,198,73,40,14,146,51,77,11,52,80,56,135,55,64,60,72,72,71,6,124,13,222,9,251,190,47,89,119,8,39,4,6,141,109,425,197,146,76,128,53,20,66,53,11,188,100,157,177,277,70,208,248,7,76,195,53,58,110,8,135,121,23,2,116
+0.65,49,120,30,233,20,30,161,60,74,95,44,37,12,186,23,52,6,31,26,54,40,127,267,60,339,214,80,86,98,105,99,84,15,68,57,8,145,57,47,14,42,228,431,11,58,305,81,136,55,53,60,68,72,62,6,124,13,73,9,146,220,47,138,51,109,95,35,6,8,65,425,261,233,51,236,437,20,32,46,47,218,79,107,48,7,21,425,69,7,76,208,82,58,151,8,149,128,104,2,134
+0.70,193,18,69,113,20,89,108,103,152,29,47,63,12,186,28,101,7,184,26,203,40,90,87,60,305,156,209,57,104,168,77,24,15,50,208,8,145,9,76,14,42,200,458,11,58,132,64,134,55,64,60,130,272,75,6,41,13,73,9,185,154,42,71,170,9,207,89,6,20,59,135,178,233,51,309,274,20,173,58,156,218,79,173,52,7,40,200,85,321,116,74,42,58,171,8,62,67,160,2,157
+0.75,89,198,69,169,20,38,286,103,136,29,36,161,12,220,28,101,7,201,37,5,40,90,194,20,118,174,73,42,55,168,61,24,15,72,302,311,145,9,76,14,42,10,96,11,93,128,45,175,55,67,60,130,41,65,6,41,13,73,9,67,311,155,226,67,9,107,67,91,59,59,135,152,182,51,102,126,20,173,58,70,181,94,82,174,7,40,365,107,100,79,240,42,58,171,8,316,12,94,2,151
+0.80,246,49,31,202,20,32,221,103,41,29,43,32,99,58,28,157,7,63,37,5,40,90,194,122,434,270,114,21,171,152,110,24,15,72,215,82,242,9,19,14,42,148,96,15,44,59,356,146,61,41,60,48,41,65,99,49,13,73,9,96,68,89,194,67,9,198,126,37,16,19,180,240,28,298,128,115,20,68,55,49,126,81,149,154,7,36,272,124,76,79,2,59,58,165,8,224,12,84,2,151
+0.85,88,143,31,328,20,58,129,133,80,99,122,32,200,56,28,214,7,185,312,5,40,109,357,104,238,300,337,23,248,207,160,24,15,19,116,361,168,9,305,14,204,92,96,11,151,66,168,138,61,26,60,39,258,74,99,138,13,73,9,129,260,176,93,359,9,78,178,107,16,19,108,187,28,501,366,128,20,23,36,68,181,189,123,190,6,36,434,45,125,79,2,56,58,112,8,48,12,36,2,514
+0.90,206,77,31,230,20,183,59,154,47,141,336,32,200,56,103,112,7,77,232,5,211,402,49,516,270,350,361,225,213,40,255,24,15,19,138,73,402,9,354,87,23,217,96,11,70,134,185,86,61,26,196,39,177,102,53,59,13,63,9,102,248,80,276,61,9,223,430,6,16,19,436,456,28,171,266,23,20,23,148,17,210,96,64,412,6,86,399,703,147,79,2,61,4,42,8,63,20,80,2,130
+0.95,206,134,31,204,104,263,59,196,47,223,157,130,199,56,103,112,7,93,266,5,65,42,49,88,269,331,271,160,143,31,43,24,15,19,342,359,150,9,92,61,23,116,97,11,223,134,212,169,127,191,124,41,89,65,53,186,13,95,9,102,228,210,217,116,9,306,637,6,16,19,157,109,28,207,109,51,20,23,358,17,91,217,210,176,6,158,321,77,227,75,2,18,4,54,8,268,340,35,2,304
+1.00,25,180,84,257,471,116,40,386,47,329,60,103,368,56,164,141,7,66,169,152,68,184,49,502,379,9,212,114,138,31,43,24,15,22,306,523,159,9,209,266,23,116,92,16,102,47,438,139,97,154,63,235,105,65,63,61,13,918,9,224,21,169,162,116,303,242,115,6,16,19,738,238,28,268,126,218,20,23,58,173,91,155,53,269,6,295,347,190,287,46,2,189,4,42,257,65,271,35,2,252

rawResults/convergenceSimpleNegRewInJumpQLearning099disc09LR.txt

@@ -0,0 +1,21 @@
+# Simple, Dist and inJump, QLearning 0.99 disc 0.9 Learning rate, -1 reward when in jump and jumping
+0.05,6,6,10,8,8,7,6,10,8,6,8,7,8,6,10,10,9,9,12,7,7,8,9,11,8,12,7,8,8,8,9,8,10,7,10,8,9,9,7,7,8,7,7,5,11,8,8,7,8,7,8,8,7,7,9,8,6,5,10,10,7,10,8,7,7,7,7,11,8,9,10,8,10,8,7,6,5,8,7,8,8,8,3,8,9,9,4,8,6,11,7,8,8,8,9,8,12,7,10,9
+0.1,6,6,8,10,8,7,6,7,9,6,8,10,8,7,10,22,7,11,9,9,8,14,8,29,8,10,7,7,9,7,10,8,11,7,9,9,10,7,7,14,7,7,10,9,5,6,10,8,9,10,8,6,10,9,10,8,6,18,9,32,6,10,8,7,8,7,7,5,8,9,8,8,8,8,6,6,5,9,7,6,8,8,6,12,9,9,4,8,6,11,8,8,9,7,8,7,10,8,12,37
+0.15,7,6,5,9,7,7,6,6,8,15,7,7,13,8,8,16,7,16,12,12,8,21,7,11,7,26,4,7,10,7,10,8,7,7,12,10,6,10,6,6,6,15,20,9,5,8,8,7,10,10,7,48,10,8,12,7,7,6,8,11,6,10,9,7,8,35,8,5,10,5,14,14,10,8,12,8,7,7,7,8,8,7,7,20,9,13,4,10,6,26,12,8,8,10,7,8,11,63,13,16
+0.2,10,6,7,17,15,12,6,76,8,25,9,7,47,8,10,12,9,20,8,11,8,80,13,8,7,11,8,22,23,7,29,10,7,6,9,9,6,8,18,6,5,18,7,9,5,8,27,11,10,40,37,39,9,12,18,7,7,6,8,12,6,10,7,7,8,21,17,5,10,9,32,9,17,7,23,8,7,9,7,8,22,10,7,7,21,13,4,8,6,7,15,8,11,11,7,8,11,25,48,10
+0.25,10,6,38,8,9,7,6,58,19,9,16,7,10,15,36,21,9,20,7,10,8,11,19,6,7,14,8,47,25,7,10,13,29,7,39,8,13,8,7,6,17,27,19,9,5,15,30,11,8,47,44,44,9,66,69,53,7,12,16,9,15,12,7,7,10,21,15,10,10,13,6,9,13,17,58,9,9,8,9,14,22,11,7,7,27,13,4,16,6,7,10,8,11,11,6,27,10,40,70,42
+0.3,14,5,40,8,10,13,6,58,25,8,12,10,9,60,12,7,7,41,12,17,8,11,104,6,22,14,55,24,49,8,24,34,70,6,58,8,38,42,13,11,24,51,23,9,5,14,42,11,8,18,26,27,18,26,83,46,6,32,8,9,39,11,29,7,10,16,44,10,11,13,6,31,13,46,59,42,9,8,9,7,26,11,6,116,25,15,8,15,5,7,10,8,12,11,28,7,19,9,32,12
+0.35,14,5,134,8,11,13,60,30,24,10,15,23,9,24,53,7,8,60,12,18,9,7,35,6,48,8,7,86,49,8,9,47,47,6,64,8,50,49,12,11,112,81,72,9,5,10,35,11,5,12,27,22,9,11,41,11,9,210,48,52,40,10,65,19,10,61,9,10,12,9,6,58,12,13,45,48,9,9,9,14,43,11,6,51,26,6,7,15,40,7,10,8,12,42,11,25,19,9,51,10
+0.40,20,5,69,9,9,30,96,69,44,10,44,57,45,68,33,7,8,15,41,28,64,7,29,6,108,8,8,25,14,54,9,39,56,6,79,8,17,59,52,17,31,212,6,8,5,44,32,16,54,33,68,33,9,44,55,44,10,38,33,97,43,10,111,22,10,34,86,7,12,6,8,8,55,14,11,10,12,9,23,44,10,13,44,42,16,8,7,15,93,7,53,7,48,12,9,90,59,11,35,49
+0.45,41,5,95,9,16,9,62,84,161,10,41,30,51,27,64,7,7,45,75,23,14,25,189,6,129,48,8,40,41,8,9,98,50,6,51,8,51,28,23,41,62,47,90,28,5,83,75,16,41,62,50,33,8,6,28,38,7,38,56,9,73,42,55,30,10,52,52,6,43,6,12,29,17,30,41,57,52,9,36,8,10,12,5,53,52,8,6,42,9,7,26,38,22,33,9,35,52,133,58,152
+0.50,25,5,40,9,13,8,15,53,49,9,113,31,41,46,79,7,7,63,16,9,8,77,46,37,66,19,8,31,44,8,40,47,50,38,85,35,50,28,58,46,134,81,144,103,122,11,52,36,59,44,109,38,50,24,95,121,7,32,57,49,18,43,38,61,53,83,156,6,30,39,13,79,61,49,154,39,81,9,102,40,142,18,5,16,39,8,6,16,10,11,63,9,8,32,57,11,48,12,36,52
+0.55,50,13,70,9,67,8,37,119,200,9,80,90,123,47,116,339,10,45,113,9,8,45,86,41,85,45,8,30,43,89,13,52,50,148,65,134,7,122,188,92,45,38,151,36,55,194,46,28,58,23,85,75,74,44,56,190,7,39,104,37,18,49,73,120,8,92,36,5,34,121,11,36,71,157,36,33,405,68,90,161,112,11,7,12,49,8,38,20,10,167,33,9,10,170,70,82,93,12,167,90
+0.60,56,13,148,9,18,90,30,24,156,9,29,31,58,16,54,154,10,64,10,9,8,52,280,46,202,270,17,72,52,14,34,188,104,22,145,43,4,7,98,47,47,142,26,73,53,60,31,69,17,84,48,105,21,44,73,71,7,70,48,62,19,38,51,69,52,63,31,5,87,106,74,50,51,123,51,45,35,65,60,26,40,11,7,177,26,88,75,104,145,115,51,82,109,39,112,77,148,8,38,74
+0.65,23,13,71,10,18,98,372,121,59,9,44,147,130,100,54,158,7,111,47,32,37,171,400,40,38,50,39,32,49,310,168,79,79,82,220,76,5,7,15,61,8,43,49,217,64,49,32,107,17,212,11,122,170,44,38,40,7,30,81,47,16,286,39,130,20,152,230,5,40,150,23,178,62,147,106,41,88,127,300,75,10,14,24,188,37,173,44,59,94,80,15,45,74,87,54,35,242,90,170,112
+0.70,194,148,44,22,318,271,113,172,234,45,44,194,126,135,11,63,44,205,88,359,356,46,83,268,166,55,68,162,68,202,208,165,48,100,142,90,5,78,239,60,99,101,267,120,109,232,3,50,32,48,11,141,38,67,523,88,71,161,121,243,16,55,178,143,22,94,114,5,90,86,21,302,50,141,130,134,413,269,87,76,331,54,149,26,74,39,44,34,131,121,17,205,233,162,38,69,58,49,87,195
+0.75,269,101,76,81,79,60,113,71,111,37,41,75,38,229,19,111,81,487,59,42,466,346,142,46,143,184,64,681,118,165,56,82,50,84,73,41,5,311,36,212,274,86,51,417,41,91,3,147,47,64,80,433,618,124,156,392,113,67,164,50,626,105,64,321,22,63,38,133,73,38,42,69,222,31,37,111,281,422,252,39,93,51,274,43,50,113,89,216,126,56,58,315,42,81,185,603,190,142,74,138
+0.80,398,116,103,169,206,85,178,100,235,37,663,113,38,152,127,511,44,124,78,64,130,139,46,141,43,186,378,466,58,88,12,93,242,178,461,151,252,210,105,97,436,110,246,293,46,174,3,63,46,64,158,436,47,314,102,145,94,76,144,45,46,14,438,37,133,155,19,50,211,70,70,216,94,31,561,110,14,285,43,41,193,26,148,92,84,44,41,75,126,24,55,111,504,215,989,196,11,205,180,346
+0.85,295,484,216,49,211,290,254,247,478,38,98,865,118,79,10,115,114,412,195,100,230,286,51,847,79,49,28,168,49,505,188,93,357,180,103,235,182,537,214,96,172,10,66,449,43,174,3,293,14,97,848,687,214,178,68,316,96,22,74,71,219,14,211,110,523,57,73,76,157,43,50,67,114,31,48,132,38,448,441,148,379,207,472,229,404,79,59,35,148,24,94,45,898,55,50,58,10,110,43,675
+0.90,93,904,325,74,134,72,510,107,174,146,41,197,41,166,136,196,885,100,419,501,135,34,88,134,129,48,92,230,67,872,322,58,498,172,263,97,104,208,58,159,465,527,138,397,91,181,3,217,14,58,167,81,68,86,165,140,408,54,148,676,934,96,74,55,66,242,100,164,363,39,113,495,158,210,307,92,248,251,40,147,548,310,237,79,315,70,379,121,78,24,98,420,396,456,51,41,202,50,215,87
+0.95,108,119,478,182,105,663,183,99,189,89,376,784,450,52,91,306,54,350,228,188,41,200,779,200,937,41,718,247,45,275,427,129,616,77,54,618,395,210,417,224,644,16,145,129,348,165,3,205,13,514,517,49,128,274,648,410,240,417,486,75,119,812,211,74,96,168,61,786,267,1067,185,94,187,106,236,146,154,505,56,117,79,332,276,355,724,237,525,98,86,63,72,24,406,147,522,470,1000,146,267,132
+1.00,188,947,522,29,49,69,499,165,795,127,829,17,125,27,115,557,77,56,54,132,144,624,759,309,282,41,178,314,98,524,126,274,273,216,77,128,99,774,67,187,337,197,289,121,102,98,3,58,201,343,234,1074,114,237,442,86,93,161,323,331,184,444,307,429,96,49,61,104,631,290,156,312,283,85,493,108,191,329,47,187,135,159,378,499,703,237,1236,663,375,754,204,24,39,158,310,267,207,23,957,534

rawResults/convergenceSimpleNoJumpInfoMCfirstVisit.txt

@@ -0,0 +1,21 @@
+#same speed and distance. ONLY DISTANCE TO OBSTACLE. 100 trials, rest did not converged!; MONTE CARLO
+0.05,8,64,62,15,84,5,72,183
+0.1,15,15,6,18,46,28,307
+0.15,177,20,12,9,21,4,6,65,173
+0.2,6,56,13,28,4,231,9,11,22
+0.25,92,37,11,4,310,1023,3676
+0.3,515,106,7,302,12,23,4,5,71
+0.35,366,23,28,1357,19,49,4,5,67,120
+0.40,11,21,113,32,4
+0.45,3,4,280,9
+0.50,11,15,4,9
+0.55,2,15,15,4,15,27,77
+0.60,2,15,4,167,14
+0.65,2,158,5,4,77,14,4
+0.70,2,3,4,2,5,123,4
+0.75,27,3,3,2,122,139,4,29,17,4
+0.80,36,522,3,49,4,26,4,3
+0.85,36,136,28,3,4,51,11,4,4,3
+0.90,63,27,76,85,106,4,38,977,9,4,3
+0.95,28,61824,110,389,61,32,740,746,8,3,4,21,2937,707,23,23,3104,9082,9,2506,118,324,3
+1.00,11829,50271,238,111576,14458,28031,9152,12999,171,35140,6443,123,3589,7190,8798,5004,35250,3904,52144,67843,15573,26838,31676,25,1497,30052,8964,99392,47321,866,27500,3302,780,49166,49633,33120,158,36720,5189,110727,25180,2891,36822,5678,1666,15222,149967,3,8185,29538,9410,47919,123796,4,44800,3975,27851,4592,58855,41441,68739,23084,20831,43449,29411,1188,26,211,158286,241,3834,46305,783,119,1051,7542,4,3154,711,9,19731,170374,174,176,9852,139312,11988,1464,10136

rawResults/convergenceSimpleNoJumpingQLearning099disc09LR.txt

@@ -0,0 +1,21 @@
+#Same speed and distance; JUST DISTANCE. Q Learning 0.99 discount, 0.9 Learning rate
+0.05,172,208,935,127,181,72,302,137,372,185,371,95,181,65,686,36,34,137,784,644,93,58,228,177,1049,55,168,590,133,588,259,323,62,63,1157,87,88,89,103,284,234,457,125,54,587,32,379,89,128,72,46,608,78,101,183,104,429,122,45,494,217,68,88,139,309,1044,83,133,65,220,129,84,133,97,298,231,189,780,50,286,126,64,137,800,28,77,245,45,144,251,303,415,282,224,57,299,33,337,126,31
+0.1,130,62,978,385,33,137,443,283,103,457,582,324,621,261,23,819,20,74,399,56,83,30,66,456,813,232,635,104,39,391,619,235,112,272,528,78,63,54,128,39,431,488,390,114,78,159,701,463,127,60,187,163,37,60,214,226,239,421,77,28,394,263,308,371,121,49,56,173,65,433,57,77,81,56,31,111,381,98,1012,280,643,296,34,75,331,872,204,41,63,208,234,230,168,303,70,94,48,83,196,183
+0.15,47,293,211,768,234,194,366,30,212,71,75,144,115,316,317,110,522,136,249,254,84,76,139,237,110,81,162,433,455,36,83,127,179,24,168,41,27,102,196,185,488,114,149,309,373,86,33,418,223,131,198,366,405,44,77,63,143,138,26,113,299,111,66,268,215,41,23,175,235,36,35,76,399,262,502,470,556,682,119,171,89,633,196,85,284,109,47,342,161,62,329,427,511,223,216,74,607,108,348,253
+0.2,358,129,308,337,180,51,95,33,241,320,94,148,54,70,97,423,69,133,171,453,74,539,51,25,856,68,65,124,100,234,563,83,24,204,73,167,50,36,96,281,345,40,326,69,1464,387,424,161,159,456,48,1222,105,245,381,102,78,172,182,571,167,213,525,364,80,118,756,34,228,258,29,359,72,63,34,108,489,182,167,304,266,213,201,416,85,101,215,109,286,83,489,308,317,48,640,603,317,333,241,130
+0.25,881,474,109,194,30,203,103,31,258,87,122,380,75,422,134,51,106,66,555,48,505,96,144,111,104,358,620,175,114,116,120,59,78,78,205,96,592,226,198,183,174,63,45,92,34,115,398,301,110,121,78,607,137,310,111,169,170,29,150,82,207,242,633,215,310,44,378,128,486,66,70,335,99,434,425,494,36,65,31,213,53,193,387,112,293,171,60,118,150,109,47,341,229,97,80,94,363,160,55,299
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+0.35,75,579,76,155,57,193,252,90,279,217,203,34,125,202,96,221,313,498,135,308,470,115,376,59,30,185,51,89,123,276,265,121,154,100,82,169,34,199,295,163,199,170,102,56,294,48,139,287,81,232,1221,70,51,164,42,366,24,172,138,176,803,133,41,123,130,270,120,187,219,48,578,57,288,157,746,815,664,104,231,415,30,74,138,82,487,475,105,171,286,100,764,266,200,576,482,81,87,86,563,64
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+0.75,286,84,151,36,108,452,89,180,139,57,497,409,250,282,40,96,305,97,711,119,78,175,37,493,180,41,60,410,170,277,71,262,59,180,132,213,316,51,489,51,274,83,41,489,340,35,129,34,807,44,214,141,374,160,103,531,80,187,58,665,139,79,43,68,289,164,1448,39,102,84,223,39,370,47,45,103,49,100,74,246,128,160,85,216,317,134,111,59,48,251,79,44,112,259,35,159,52,72,279,51
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rawResults/convergenceSimpleNoJumpingSARSA099disc09LR.txt

@@ -0,0 +1,21 @@
+#Same speed and distance; JUST DISTANCE. SARSA 0.99 discount, 0.9 Learning rate
+0.05,139,65,1289,183,68,166,159,404,110,175,564,148,328,167,302,72,296,139,32,186,291,349,61,51,277,425,86,271,97,436,176,185,293,153,33,1012,398,56,188,151,71,107,148,85,172,89,56,614,1252,282,53,47,146,37,48,389,168,180,66,33,78,300,113,591,712,175,884,85,194,127,180,239,667,167,136,67,86,1253,303,126,85,361,206,214,581,134,89,55,372,199,182,308,84,87,140,269,527,63,24,64
+0.1,34,77,113,154,282,70,75,592,391,49,116,217,33,80,317,68,84,248,28,94,276,144,223,269,39,178,364,366,72,130,27,131,187,130,126,75,399,207,267,217,410,379,307,115,253,302,313,80,98,67,410,106,68,30,296,83,383,33,50,103,191,167,139,76,647,90,468,98,209,91,45,115,96,78,24,81,158,63,35,82,96,57,100,119,829,111,67,38,60,220,144,37,148,516,58,126,288,415,102,146
+0.15,172,181,101,220,338,54,328,136,193,302,786,117,45,41,57,27,230,293,49,354,149,190,47,223,75,41,143,110,59,577,163,329,169,572,58,96,451,208,51,50,172,67,287,98,104,22,146,520,315,344,110,275,52,30,110,36,28,584,557,186,142,211,75,126,316,60,484,73,178,83,151,61,305,293,164,156,190,136,70,107,113,81,36,28,207,35,121,25,127,655,146,86,74,114,152,582,232,29,138,123
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rawResults/convergenceSimpleQ099Disc09LRSameRewardsAsMC.txt

@@ -0,0 +1,21 @@
+# +1 per timestamp, +0 when hitting the obstacle
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rawResults/convergenceSimpleQ099Disc09LRSameRewardsAsMCMinus1DefaultValue.txt

@@ -0,0 +1,21 @@
+#integer max value
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+0.40,66,125,150,2,18,296,213,149,9,34,179,151,127,426,55,116,155,160,26,131,282,16,17,67,49,460,141,131,57,206,247,69,138,19,183,6,48,93,260,107,280,124,30,41,574,69,376,17,86,57,137,75,167,189,146,89,317,35,136,218,335,39,117,255,101,12,301,104,36,71,109,153,34,134,151,60,429,97,130,24,272,75,167,95,307,42,124,284,24,11,164,68,740,86,140,274,468,146,644,263
+0.45,25,5,82,2,341,83,281,95,9,522,20,13,138,72,142,177,89,237,48,14,200,39,379,376,114,261,304,246,141,483,45,52,114,19,113,102,459,77,131,248,79,91,101,56,113,271,53,17,152,10,509,263,419,77,199,83,139,91,30,88,193,31,69,73,335,90,82,32,67,234,57,106,165,159,330,140,21,28,633,253,472,143,550,17,13,291,105,92,609,7,1123,243,170,25,35,95,218,901,79,358
+0.50,169,470,270,2,133,72,277,110,49,34,32,96,5,309,605,109,49,646,27,14,62,80,198,60,20,57,354,352,231,488,20,39,206,113,45,99,254,124,138,141,17,544,345,575,36,23,20,17,456,10,153,39,78,31,66,206,131,406,435,10,97,117,175,272,179,700,364,114,309,181,82,65,188,34,99,71,109,33,284,102,47,66,140,238,149,80,207,26,209,717,171,307,54,27,14,287,272,311,95,120
+0.55,521,298,87,2,121,18,75,156,81,133,118,179,45,196,114,94,218,148,98,106,477,138,126,190,81,134,128,419,126,277,389,51,784,164,147,81,382,298,85,196,317,99,112,85,358,387,24,275,199,23,27,98,12,119,84,632,34,35,178,464,62,167,182,479,40,257,155,43,64,144,58,222,116,41,224,253,54,11,34,235,14,13,54,40,13,221,148,41,35,143,269,59,441,257,81,7,23,23,85,387
+0.60,258,167,170,2,106,65,871,706,267,460,32,51,241,83,135,32,329,459,456,125,130,288,157,84,125,295,173,55,50,77,182,55,193,177,153,122,144,60,198,805,286,81,53,90,8,156,107,4,260,385,122,150,300,154,95,319,46,30,389,125,67,222,22,276,77,8,55,141,19,39,103,16,177,44,58,265,88,154,11,172,53,269,55,31,445,304,28,543,28,78,223,46,146,39,19,7,77,105,16,54
+0.65,162,47,406,2,48,173,28,245,123,153,330,717,45,50,49,424,60,186,35,56,56,21,72,31,371,62,316,47,120,19,99,107,47,18,59,67,70,42,405,24,213,30,141,131,218,45,59,4,46,98,154,197,220,60,53,272,326,29,63,22,33,355,21,51,57,175,251,536,201,25,25,56,241,90,65,56,79,185,365,138,43,220,86,31,244,197,207,242,9,368,217,88,436,174,34,443,623,145,130,63
+0.70,73,388,31,241,414,78,102,156,267,224,162,47,398,50,140,12,380,118,70,236,319,42,121,104,527,6,79,87,59,141,12,19,294,866,25,58,36,97,467,61,29,93,87,136,29,123,56,4,183,120,178,16,344,314,109,43,137,223,246,42,167,197,63,151,265,36,162,264,615,17,62,100,192,90,195,192,232,153,19,396,233,20,687,392,349,77,113,227,365,126,51,24,95,18,180,132,191,28,208,135
+0.75,198,83,164,28,112,76,216,165,336,85,27,203,192,166,130,12,314,91,122,404,39,10,18,16,165,6,322,330,162,70,496,51,83,217,410,49,20,251,130,38,220,103,139,79,186,471,19,4,149,388,460,120,313,23,46,343,164,220,232,269,48,28,64,41,65,168,121,138,269,21,136,215,184,118,103,44,29,362,474,40,271,70,184,168,196,60,57,129,50,167,60,23,326,81,46,221,342,28,87,44
+0.80,199,262,215,159,17,80,263,88,279,53,205,67,264,133,218,12,484,224,42,166,39,10,210,59,161,6,81,123,74,18,12,72,114,237,331,64,448,469,163,150,41,106,174,408,35,116,106,4,641,421,86,230,4,57,249,284,326,234,104,86,81,125,23,156,4,85,66,332,660,28,60,73,96,40,305,59,247,84,407,247,87,831,198,26,497,214,70,108,13,117,29,119,95,127,96,51,36,43,316,167
+0.85,107,180,240,95,421,27,148,242,55,55,212,18,39,78,22,12,27,193,26,12,404,86,345,147,143,89,86,193,94,350,12,272,99,16,201,262,66,215,92,226,60,68,180,82,178,47,44,4,6,146,183,253,4,116,144,9,63,141,54,165,161,285,336,209,20,413,76,215,116,681,66,277,24,349,259,126,438,193,192,14,160,205,92,499,140,148,104,202,13,20,433,169,56,291,119,65,360,282,18,342
+0.90,180,194,235,289,428,115,32,20,123,27,71,18,112,163,27,309,177,308,127,12,134,24,219,656,313,92,259,248,451,87,121,34,44,18,12,605,118,198,232,351,311,113,411,59,436,76,395,4,6,24,42,119,4,59,19,47,130,154,311,144,57,50,45,115,79,264,251,473,130,176,481,240,70,37,344,75,52,193,412,14,42,324,468,30,112,75,213,620,99,360,423,119,164,40,51,211,250,265,24,50
+0.95,132,220,36,35,253,181,115,142,10,39,151,24,66,355,159,151,134,99,58,75,298,60,67,158,310,6,102,20,110,304,100,274,588,178,515,95,74,157,18,78,233,128,64,167,522,62,388,4,14,60,33,22,4,448,19,258,71,116,63,497,133,50,309,77,37,409,469,48,201,221,134,24,656,60,106,28,39,88,357,502,44,225,200,113,290,153,269,251,373,249,344,93,114,154,166,30,224,123,1075,106
+1.00,197,149,103,28,105,139,189,314,41,107,15,24,34,26,17,77,10,103,82,108,42,262,71,23,343,228,109,96,139,16,51,366,33,78,18,80,344,33,117,31,189,166,197,100,111,64,52,4,218,129,33,4,4,103,112,320,88,188,387,122,132,405,57,343,182,212,207,318,48,148,181,312,238,196,24,29,259,215,83,284,149,252,161,468,187,27,218,97,98,516,230,110,443,62,78,110,62,39,146,82

rawResults/convergenceSimpleSARSA099disc09LR.txt

@@ -0,0 +1,21 @@
+#same speed and distance. Distance and inJump SARSA; 0.99discount factor 0.9 learning rate
+0.05,51,48,43,43,34,47,41,38,41,38,57,35,37,33,37,39,36,36,43,37,40,37,48,36,41,37,38,44,49,42,41,37,38,36,37,43,40,41,46,32,40,54,44,39,42,34,44,36,40,60,34,45,40,42,40,41,36,43,41,36,41,36,54,38,34,42,42,36,41,34,43,36,45,39,37,51,40,42,41,37,39,35,39,34,41,38,65,37,35,39,39,41,47,40,53,39,33,38,40,34
+0.1,44,39,48,58,40,40,35,39,48,46,40,39,35,45,45,41,46,44,43,50,46,43,47,42,47,45,41,39,46,43,41,37,40,46,39,47,41,41,38,37,36,46,41,42,47,35,38,45,44,44,38,44,44,37,47,38,36,44,41,43,43,41,42,47,42,41,44,36,36,35,44,44,38,39,35,43,38,40,48,41,41,33,46,35,40,41,44,46,52,41,39,42,50,36,40,50,40,71,42,41
+0.15,41,41,66,38,41,44,42,67,53,37,54,47,40,35,45,42,48,40,44,47,59,45,42,39,34,47,44,88,68,49,48,41,42,41,38,41,36,52,41,68,37,45,56,39,47,39,40,57,42,34,46,49,42,39,39,42,42,49,50,42,40,50,58,39,48,37,42,37,36,44,50,42,44,44,39,44,47,51,42,45,57,37,38,56,48,48,36,40,45,44,38,50,42,37,40,50,38,66,39,39
+0.2,47,41,62,66,35,41,83,40,52,42,57,44,53,44,56,34,41,40,50,57,46,49,40,52,35,37,58,46,39,56,50,57,43,44,47,35,44,47,42,35,43,51,56,45,55,45,49,42,46,56,42,49,50,53,33,50,39,44,51,38,35,52,35,41,53,37,41,54,60,40,42,40,49,44,47,54,66,41,38,52,47,49,38,39,62,67,48,34,47,46,51,38,43,47,41,36,52,54,46,54
+0.25,57,49,41,73,41,35,70,42,60,38,42,41,48,68,44,36,57,56,78,47,39,62,42,52,58,52,80,75,63,43,42,44,44,43,52,57,51,41,41,49,50,38,42,55,74,62,104,45,50,56,33,41,46,36,42,111,41,52,44,105,52,75,45,49,74,54,38,62,67,86,58,37,36,42,41,100,83,51,69,43,46,58,57,41,54,52,53,42,41,56,77,47,40,42,43,45,39,63,47,57
+0.3,41,41,92,47,56,80,46,77,81,45,46,48,50,48,59,54,54,36,57,38,52,65,69,101,35,43,45,44,42,47,54,77,45,42,43,54,50,58,38,38,54,42,41,72,54,45,61,41,45,47,95,54,108,33,69,48,49,73,41,61,57,45,45,58,42,54,59,49,48,51,43,40,32,36,79,53,53,44,58,43,54,61,66,47,63,38,43,50,56,45,46,52,42,36,51,44,65,38,38,51
+0.35,42,41,46,99,36,64,46,49,91,53,48,60,70,58,89,36,56,39,64,38,56,50,42,41,64,67,52,89,53,92,45,45,50,42,78,42,74,56,81,52,41,44,59,74,77,54,48,59,52,52,46,50,51,57,45,36,42,31,62,58,104,65,73,34,66,53,49,71,55,35,54,42,41,70,68,46,43,83,42,44,39,78,55,38,48,54,56,47,53,38,54,43,47,56,76,50,50,44,44,42
+0.40,44,80,44,48,38,90,58,51,43,48,63,45,50,60,189,72,50,48,60,41,51,41,53,49,69,66,66,60,44,42,51,66,41,41,52,53,63,62,40,39,45,59,38,107,116,48,79,48,46,39,44,44,31,42,41,47,34,46,58,59,31,44,57,44,45,44,33,104,70,47,136,43,47,64,75,51,63,59,59,42,48,114,35,64,63,46,43,45,41,42,52,71,59,42,101,96,53,56,45,38
+0.45,43,47,84,39,100,86,54,45,58,54,52,63,44,71,60,36,63,73,106,39,96,44,142,66,50,54,114,48,41,44,59,48,45,67,79,57,55,42,82,60,53,89,53,49,52,112,53,37,63,42,46,48,63,48,57,86,63,43,76,51,48,72,49,55,35,46,30,43,78,51,53,108,43,60,63,63,81,48,93,40,37,59,37,43,79,72,133,44,85,49,65,42,100,55,112,68,70,53,59,39
+0.50,50,44,68,78,52,95,76,49,58,46,95,34,61,74,80,61,41,141,80,49,49,35,93,54,55,53,119,51,89,50,88,64,102,45,94,71,74,44,45,101,93,92,40,46,61,60,46,84,52,101,86,48,91,135,124,56,47,49,50,93,93,46,41,74,80,48,36,38,51,75,110,59,51,43,141,52,45,77,53,35,49,36,80,44,67,53,38,61,67,45,64,48,43,80,42,78,52,56,40,62
+0.55,58,65,50,107,42,73,48,112,51,110,76,35,41,101,83,110,46,135,104,137,47,50,49,86,50,40,41,63,126,60,130,47,43,40,88,152,48,83,113,113,51,112,98,103,46,42,62,94,115,71,34,56,56,58,102,35,73,87,44,52,74,69,52,50,79,43,41,138,53,38,84,55,48,35,41,42,103,55,74,55,61,61,89,106,42,54,47,54,71,47,62,41,86,75,103,63,65,56,123,47
+0.60,84,90,53,61,37,81,82,110,87,103,113,36,50,57,45,64,97,49,41,85,103,97,165,42,44,59,95,44,62,39,185,76,39,51,63,42,66,89,66,68,42,62,64,83,79,65,43,74,77,63,61,40,38,50,92,52,49,105,45,45,50,39,58,82,98,53,72,98,106,30,141,117,43,53,84,110,89,32,100,38,51,116,56,180,84,79,66,72,66,93,128,73,80,88,118,37,141,95,113,36
+0.65,73,59,44,88,43,64,124,55,53,79,77,35,137,88,81,110,104,54,567,75,45,122,48,53,91,60,145,116,163,99,146,101,61,83,89,208,48,47,72,96,49,82,102,144,50,75,73,61,91,103,172,109,63,56,99,49,91,106,58,63,36,90,72,79,92,47,87,103,97,95,80,95,43,79,62,73,115,32,84,66,73,73,137,127,125,85,114,85,93,41,97,40,117,45,82,35,124,37,86,76
+0.70,85,48,94,73,195,111,71,66,78,132,78,133,46,87,38,163,72,44,88,60,144,35,57,88,178,43,119,123,152,76,96,91,128,60,93,90,70,84,122,81,112,215,99,69,64,79,98,104,59,168,57,38,36,55,94,54,67,75,63,90,46,39,53,86,83,78,104,47,49,55,57,48,50,196,56,220,204,32,47,69,75,43,50,84,174,94,107,130,142,51,171,49,77,44,66,146,118,84,58,167
+0.75,170,87,112,224,135,139,118,58,176,95,108,127,46,83,191,114,83,186,143,237,53,117,85,134,104,93,56,124,280,63,96,57,34,66,244,372,152,52,51,116,80,91,141,120,161,51,82,80,56,107,89,47,69,86,42,115,76,52,369,114,47,69,57,80,74,103,80,72,124,92,51,87,73,91,47,97,169,368,64,137,118,133,94,88,72,49,132,68,320,234,184,111,100,68,120,45,49,124,138,75
+0.80,96,270,37,41,288,108,134,86,73,122,51,108,164,77,121,276,62,50,133,94,262,125,116,129,68,69,140,209,72,68,149,103,61,207,85,283,121,124,47,63,48,113,137,137,131,115,91,98,340,202,81,130,86,74,267,45,93,73,127,54,235,67,40,93,171,186,64,103,65,64,231,109,93,79,284,93,249,60,116,65,185,170,170,278,79,39,134,43,305,122,63,46,180,43,70,169,119,128,122,222,
+0.85,274,207,54,91,298,279,37,68,96,119,137,160,84,202,215,76,247,206,149,85,98,261,129,106,293,104,98,129,49,161,119,137,120,75,99,101,180,83,60,103,112,106,114,84,94,59,109,99,242,56,114,45,99,97,207,150,226,205,72,162,198,241,129,110,108,96,121,105,237,73,195,204,71,203,249,101,181,67,335,91,66,197,153,151,69,53,223,119,221,152,65,151,112,101,116,254,149,60,126,187
+0.90,155,104,152,60,80,99,204,96,69,83,50,43,111,152,192,124,75,44,166,267,120,52,84,124,69,137,93,164,226,80,140,76,62,103,159,137,190,137,107,236,994,199,509,98,55,165,151,83,261,95,69,155,120,283,63,401,109,227,152,239,58,153,164,87,42,129,244,211,130,96,60,201,82,137,182,279,58,162,92,112,136,75,71,106,245,221,495,125,144,222,172,60,365,92,391,265,40,81,339,66
+0.95,210,306,45,157,117,114,92,139,40,70,75,55,310,246,154,233,205,65,157,101,100,70,309,182,332,111,93,200,413,36,124,366,93,189,178,1058,90,314,92,184,191,119,131,60,294,99,244,93,78,108,215,63,189,253,122,397,80,210,86,89,162,157,119,301,165,83,153,127,71,185,171,197,74,124,81,513,83,146,44,226,100,188,132,110,157,154,408,98,102,260,114,81,129,51,190,363,222,87,99,176
+1.00,96,101,152,104,613,94,217,91,173,619,288,143,370,53,348,199,201,71,68,233,129,80,81,337,295,54,313,78,132,97,61,235,339,130,98,60,224,303,190,558,244,178,211,112,119,237,327,326,351,78,117,164,71,152,143,203,123,121,143,234,539,105,268,92,185,80,125,139,111,235,151,183,159,173,304,384,101,463,210,82,168,128,181,145,170,118,121,439,90,266,139,126,328,175,236,91,107,103,57,208

rawResults/totalTimestampsNeededForFoodDiffDiscounts.txt

@@ -0,0 +1,9 @@
+# timestamp needed for x*1000 collected food; Discount factor for rows: 0.05, 0.1, 0.3, 0.5, 0.7, 0.9, 0.95, 0.99
+103856,152357,201640,245789,290299,332701,377803,419699,462803,503563,544563,583697,630453,674735,720569,765511,806589,850249,893061,934733,978755,1019951,1062057,1103441,1145615,1188351,1234143,1277625,1325757,1369897
+86806,119125,148442,174952,200616,224964,249958,273930,298390,322642,346770,370528,395712,420584,445870,470226,494548,518810,543070,567516,592404,616706,641572,665808,690356,715222,741116,765988,792200,816832
+81070,112021,138091,162895,186091,208761,232135,254523,277189,299965,322397,344691,368009,391027,414467,437437,459939,482715,505407,527789,550967,573899,596997,619723,642307,665369,688917,711741,735413,758143
+92436,122398,148442,172907,196103,218773,242147,264535,287201,309977,332409,354703,378021,401039,424479,447449,469951,492727,515419,537801,560979,583911,607009,629735,652319,675381,698929,721753,745425,768155
+120381,150374,176528,201046,224242,246912,270286,292674,315340,338116,360548,382842,406160,429178,452618,475588,498090,520866,543558,565940,589118,612050,635148,657874,680458,703520,727068,749892,773564,796294
+281911,319095,347920,374727,398147,421165,444593,467024,489742,512544,534988,557340,580754,603785,627233,650203,672711,695493,718191,740582,763766,786702,809804,832540,855124,878186,901736,924571,948245,970983
+536914,591119,634038,667263,693572,716643,740356,762956,785631,808441,830919,853232,876907,900157,923758,946822,969421,992287,1015599,1038190,1061385,1084935,1108289,1131024,1153628,1176694,1200243,1223097,1246795,1269555
+2205810,2493911,2680530,2809098,2917584,2971306,3039162,3097289,3150948,3190386,3235063,3263814,3318622,3351973,3378359,3403715,3428527,3453655,3478711,3503423,3529049,3554365,3579917,3605029,3629903,3655349,3681399,3706569,3732729,3757787

src/main/java/core/RNG.java

@@ -15,18 +15,26 @@ import java.util.Random;
  */
 public class RNG {
     private static Random rng;
+    private static Random rngEnv;
     private static int seed = 123;
     static {
         rng = new Random();
-        rng.setSeed(seed);
+        setSeed(seed, true);
     }
 
     public static Random getRandom() {
         return rng;
     }
 
-    public static void setSeed(int seed){
+    public static Random getEnvRandom() {
+        return rngEnv;
+    }
+
+    public static void setSeed(int seed, boolean setEnvSeed) {
         RNG.seed = seed;
         rng.setSeed(seed);
+        if(setEnvSeed) {
+            rngEnv.setSeed(seed);
+        }
     }
 }

src/main/java/core/algo/EpisodicLearning.java

@@ -5,7 +5,6 @@ import core.Environment;
 import core.LearningConfig;
 import core.StepResult;
 import core.listener.LearningListener;
-import core.policy.EpsilonGreedyPolicy;
 import example.DinoSampling;
 import lombok.Getter;
 import lombok.Setter;
@@ -81,18 +80,6 @@ public abstract class EpisodicLearning<A extends Enum> extends Learning<A> imple
 
     private void dispatchEpisodeStart(){
         ++currentEpisode;
-        /*
-            2f 0.02 => 100
-            1.5f 0.02 => 75
-            1.4f 0.02 => fail
-            1.5f 0.1 => 16 !
-        */
-//        if(this.policy instanceof EpsilonGreedyPolicy){
-//            float ep = 2f/(float)currentEpisode;
-//            if(ep < 0.02) ep = 0;
-//            ((EpsilonGreedyPolicy<A>) this.policy).setEpsilon(ep);
-//            System.out.println(ep);
-//        }
         episodesToLearn.decrementAndGet();
         for(LearningListener l: learningListeners){
             l.onEpisodeStart();
@@ -105,7 +92,7 @@ public abstract class EpisodicLearning<A extends Enum> extends Learning<A> imple
         timestamp++;
         timestampCurrentEpisode++;
         // TODO: more sophisticated way to check convergence
-        if(timestampCurrentEpisode > 30000000){
+        if(timestampCurrentEpisode > 50000) {
             converged = true;
             // t
             File file = new File(DinoSampling.FILE_NAME);
@@ -128,6 +115,7 @@ public abstract class EpisodicLearning<A extends Enum> extends Learning<A> imple
     private void startLearning(){
         dispatchLearningStart();
         while(episodesToLearn.get() > 0){
+
             dispatchEpisodeStart();
             timestampCurrentEpisode = 0;
             nextEpisode();

src/main/java/core/algo/Method.java

@@ -5,5 +5,5 @@ package core.algo;
  * which RL-algorithm should be used.
  */
 public enum Method {
-    MC_CONTROL_FIRST_VISIT, SARSA_EPISODIC, Q_LEARNING_OFF_POLICY_CONTROL
+    MC_CONTROL_FIRST_VISIT, MC_CONTROL_EVERY_VISIT, SARSA_ON_POLICY_CONTROL, Q_LEARNING_OFF_POLICY_CONTROL
 }

src/main/java/core/algo/mc/MonteCarloControlEGreedy.java

@@ -8,37 +8,22 @@ import core.policy.Policy;
 import org.apache.commons.lang3.tuple.ImmutablePair;
 import org.apache.commons.lang3.tuple.Pair;
 
-import java.io.*;
-import java.net.URI;
-import java.nio.file.Files;
-import java.nio.file.Path;
-import java.nio.file.Paths;
-import java.nio.file.StandardOpenOption;
+import java.io.IOException;
+import java.io.ObjectInputStream;
+import java.io.ObjectOutputStream;
 import java.util.*;
 
 /**
- * TODO: Major problem:
- * StateActionPairs are only unique accounting for their position in the episode.
- * For example:
- * <p>
- * startingState -> MOVE_LEFT : very first state action in the episode i = 1
- * image the agent does not collect the food and does not drop it onto start, the agent will receive
- * -1 for every timestamp hence (startingState -> MOVE_LEFT) will get a value of -10;
- * <p>
- * BUT image moving left from the starting position will have no impact on the state because
- * the agent ran into a wall. The known world stays the same.
- * Taking an action after that will have the exact same state but a different action
- * making the value of this stateActionPair -9 because the stateAction pair took place on the second
- * timestamp, summing up all remaining rewards will be -9...
- * <p>
- * How to encounter this problem?
- *
+ * Includes both variants of Monte-Carlo methods
+ * Default method is First-Visit.
+ * Change to Every-Visit by setting flag "useEveryVisit" in the constructor to true.
  * @param <A>
  */
-public class MonteCarloControlFirstVisitEGreedy<A extends Enum> extends EpisodicLearning<A> {
+public class MonteCarloControlEGreedy<A extends Enum> extends EpisodicLearning<A> {
 
     private Map<Pair<State, A>, Double> returnSum;
     private Map<Pair<State, A>, Integer> returnCount;
+    private boolean isEveryVisit;
 
     // t
     private float epsilon;
@@ -46,8 +31,9 @@ public class MonteCarloControlFirstVisitEGreedy<A extends Enum> extends Episodic
     private Policy<A> greedyPolicy = new GreedyPolicy<>();
 
 
-    public MonteCarloControlFirstVisitEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, int delay) {
+    public MonteCarloControlEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, int delay, boolean useEveryVisit) {
         super(environment, actionSpace, discountFactor, delay);
+        isEveryVisit = useEveryVisit;
         // t
         this.epsilon = epsilon;
         this.policy = new EpsilonGreedyPolicy<>(epsilon);
@@ -56,7 +42,11 @@ public class MonteCarloControlFirstVisitEGreedy<A extends Enum> extends Episodic
         returnCount = new HashMap<>();
     }
 
-    public MonteCarloControlFirstVisitEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, int delay) {
+    public MonteCarloControlEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, float discountFactor, float epsilon, int delay) {
+        this(environment, actionSpace, discountFactor, epsilon, delay, false);
+    }
+
+    public MonteCarloControlEGreedy(Environment<A> environment, DiscreteActionSpace<A> actionSpace, int delay) {
         this(environment, actionSpace, LearningConfig.DEFAULT_DISCOUNT_FACTOR, LearningConfig.DEFAULT_EPSILON, delay);
     }
 
@@ -104,35 +94,47 @@ public class MonteCarloControlFirstVisitEGreedy<A extends Enum> extends Episodic
         }
 
         //  System.out.printf("Episode %d \t Reward: %f \n", currentEpisode, sumOfRewards);
-        Set<Pair<State, A>> stateActionPairs = new LinkedHashSet<>();
+        HashMap<Pair<State, A>, List<Integer>> stateActionPairs = new LinkedHashMap<>();
 
+        int firstOccurrenceIndex = 0;
         for(StepResult<A> sr : episode) {
-            stateActionPairs.add(new ImmutablePair<>(sr.getState(), sr.getAction()));
+            Pair<State, A> pair = new ImmutablePair<>(sr.getState(), sr.getAction());
+            if(!stateActionPairs.containsKey(pair)) {
+                List<Integer> l = new ArrayList<>();
+                l.add(firstOccurrenceIndex);
+                stateActionPairs.put(pair, l);
+            }
+
+            /*
+            This is the only difference between First-Visit and Every-Visit.
+            When First-Visit is selected, only the first index of the occurrence is put into the list.
+            When Every-Visit is selected, every following occurrence is saved
+            into the list as well.
+             */
+            else if(isEveryVisit) {
+                stateActionPairs.get(pair).add(firstOccurrenceIndex);
+            }
+            ++firstOccurrenceIndex;
         }
-
         //System.out.println("stateActionPairs " + stateActionPairs.size());
-        for(Pair<State, A> stateActionPair : stateActionPairs) {
-            int firstOccurenceIndex = 0;
-            // find first occurance of state action pair
-            for(StepResult<A> sr : episode) {
-                if(stateActionPair.getKey().equals(sr.getState()) && stateActionPair.getValue().equals(sr.getAction())) {
-                    break;
+        for(Map.Entry<Pair<State, A>, List<Integer>> entry : stateActionPairs.entrySet()) {
+            Pair<State, A> stateActionPair = entry.getKey();
+            List<Integer> firstOccurrences = entry.getValue();
+            for(Integer firstOccurrencesIdx : firstOccurrences) {
+                double G = 0;
+                for(int l = firstOccurrencesIdx; l < episode.size(); ++l) {
+                    G += episode.get(l).getReward() * (Math.pow(discountFactor, l - firstOccurrencesIdx));
                 }
-                firstOccurenceIndex++;
+                // slick trick to add G to the entry.
+                // if the key does not exists, it will create a new entry with G as default value
+                returnSum.merge(stateActionPair, G, Double::sum);
+                returnCount.merge(stateActionPair, 1, Integer::sum);
+                stateActionTable.setValue(stateActionPair.getKey(), stateActionPair.getValue(), returnSum.get(stateActionPair) / returnCount.get(stateActionPair));
             }
-
-            double G = 0;
-            for(int l = firstOccurenceIndex; l < episode.size(); ++l) {
-                G += episode.get(l).getReward() * (Math.pow(discountFactor, l - firstOccurenceIndex));
-            }
-            // slick trick to add G to the entry.
-            // if the key does not exists, it will create a new entry with G as default value
-            returnSum.merge(stateActionPair, G, Double::sum);
-            returnCount.merge(stateActionPair, 1, Integer::sum);
-            stateActionTable.setValue(stateActionPair.getKey(), stateActionPair.getValue(), returnSum.get(stateActionPair) / returnCount.get(stateActionPair));
         }
     }
 
+
     @Override
     public void save(ObjectOutputStream oos) throws IOException {
         super.save(oos);

src/main/java/core/algo/td/QLearningOffPolicyTDControl.java

@@ -40,9 +40,9 @@ public class QLearningOffPolicyTDControl<A extends Enum> extends EpisodicLearnin
         while(envResult == null || !envResult.isDone()) {
             actionValues = stateActionTable.getActionValues(state);
             A action;
-            if(currentEpisode % 2 == 0){
+            if(currentEpisode % 2 == 0) {
                 action = greedyPolicy.chooseAction(actionValues);
-            }else{
+            } else {
                 action = policy.chooseAction(actionValues);
             }
             if(converged) return;
@@ -51,7 +51,7 @@ public class QLearningOffPolicyTDControl<A extends Enum> extends EpisodicLearnin
             double reward = envResult.getReward();
             State nextState = envResult.getState();
             sumOfRewards += reward;
-            if(currentEpisode % 2 == 0){
+            if(currentEpisode % 2 == 0) {
                 state = nextState;
                 dispatchStepEnd();
                 continue;

src/main/java/core/controller/RLController.java

@@ -7,7 +7,7 @@ import core.ListDiscreteActionSpace;
 import core.algo.EpisodicLearning;
 import core.algo.Learning;
 import core.algo.Method;
-import core.algo.mc.MonteCarloControlFirstVisitEGreedy;
+import core.algo.mc.MonteCarloControlEGreedy;
 import core.algo.td.QLearningOffPolicyTDControl;
 import core.algo.td.SARSA;
 import core.listener.LearningListener;
@@ -49,9 +49,13 @@ public class RLController<A extends Enum> implements LearningListener {
     public void start() {
         switch(method) {
             case MC_CONTROL_FIRST_VISIT:
-                learning = new MonteCarloControlFirstVisitEGreedy<>(environment, discreteActionSpace, discountFactor, epsilon, delay);
+                learning = new MonteCarloControlEGreedy<>(environment, discreteActionSpace, discountFactor, epsilon, delay);
                 break;
-            case SARSA_EPISODIC:
+            case MC_CONTROL_EVERY_VISIT:
+                learning = new MonteCarloControlEGreedy<>(environment, discreteActionSpace, discountFactor, epsilon, delay, true);
+                break;
+
+            case SARSA_ON_POLICY_CONTROL:
                 learning = new SARSA<>(environment, discreteActionSpace, discountFactor, epsilon, learningRate, delay);
                 break;
             case Q_LEARNING_OFF_POLICY_CONTROL:

src/main/java/evironment/antGame/Constants.java

@@ -3,4 +3,6 @@ package evironment.antGame;
 public class Constants {
     public static final int DEFAULT_GRID_WIDTH = 5;
     public static final int DEFAULT_GRID_HEIGHT = 5;
+    public static final int START_X = 5;
+    public static final int START_Y = 2;
 }

src/main/java/evironment/antGame/Grid.java

@@ -16,6 +16,7 @@ public class Grid {
         this.height = height;
         grid = new Cell[width][height];
         initialGrid = new Cell[width][height];
+        start = new Point(Constants.START_X, Constants.START_Y);
         initRandomWorld();
     }
 
@@ -29,10 +30,11 @@ public class Grid {
                 initialGrid[x][y] = new Cell(new Point(x, y), CellType.FREE);
             }
         }
-        start = new Point(RNG.getRandom().nextInt(width), RNG.getRandom().nextInt(height));
-        initialGrid[start.x][start.y] = new Cell(new Point(start.x, start.y), CellType.START);
         spawnNewFood(initialGrid);
         spawnObstacles();
+        initialGrid[start.x][start.y] = new Cell(new Point(start.x, start.y), CellType.START);
+        ;
+
     }
 
     //TODO
@@ -53,13 +55,13 @@ public class Grid {
     /**
      * Spawns one additional food on a random field EXCEPT for the starting position
      */
-    public void spawnNewFood(Cell[][] grid) {
+    private void spawnNewFood(Cell[][] grid) {
         boolean foodSpawned = false;
         Point potFood = new Point(0, 0);
         CellType potFieldType;
         while(!foodSpawned) {
-            potFood.x = RNG.getRandom().nextInt(width);
-            potFood.y = RNG.getRandom().nextInt(height);
+            potFood.x = RNG.getEnvRandom().nextInt(width);
+            potFood.y = RNG.getEnvRandom().nextInt(height);
             potFieldType = grid[potFood.x][potFood.y].getType();
             if(potFieldType != CellType.START && grid[potFood.x][potFood.y].getFood() == 0 && potFieldType != CellType.OBSTACLE) {
                 grid[potFood.x][potFood.y].setFood(1);

src/main/java/evironment/blackjack/cards/CardDeck.java

@@ -29,6 +29,6 @@ public class CardDeck {
             nextInt(int bound) returns random int value from (inclusive) 0
             and EXCLUSIVE! bound
           */
-        return cards.get(RNG.getRandom().nextInt(cards.size()));
+        return cards.get(RNG.getEnvRandom().nextInt(cards.size()));
     }
 }

src/main/java/evironment/jumpingDino/DinoWorld.java

@@ -44,7 +44,7 @@ public class DinoWorld implements Environment<DinoAction>, Visualizable {
     @Override
     public StepResultEnvironment step(DinoAction action) {
         boolean done = false;
-        int reward = 0;
+        int reward = 1;
 
         if(action == DinoAction.JUMP){
             dino.jump();
@@ -68,7 +68,7 @@ public class DinoWorld implements Environment<DinoAction>, Visualizable {
             spawnNewObstacle();
         }
         if(ranIntoObstacle()) {
-            reward = -1;
+            reward = 0;
             done = true;
         }
 

src/main/java/evironment/jumpingDino/DinoWorldAdvanced.java

@@ -31,7 +31,7 @@ public class DinoWorldAdvanced extends DinoWorld{
     protected void spawnNewObstacle() {
         int dx;
         int xSpawn;
-        double ran = RNG.getRandom().nextDouble();
+        double ran = RNG.getEnvRandom().nextDouble();
         if(ran < 0.25){
             dx = -(int) (0.35 * Config.OBSTACLE_SPEED);
         }else if(ran < 0.5){
@@ -41,7 +41,7 @@ public class DinoWorldAdvanced extends DinoWorld{
         } else{
             dx = -(int) (3.5 * Config.OBSTACLE_SPEED);
         }
-        double ran2 = RNG.getRandom().nextDouble();
+        double ran2 = RNG.getEnvRandom().nextDouble();
         if(ran2 < 0.25) {
             // randomly spawning more right outside of the screen
             xSpawn = Config.FRAME_WIDTH + Config.FRAME_WIDTH + Config.OBSTACLE_SIZE;

src/main/java/example/BlackJack.java

@@ -9,7 +9,7 @@ import evironment.blackjack.PlayerAction;
 
 public class BlackJack {
     public static void main(String[] args) {
-        RNG.setSeed(55);
+        RNG.setSeed(55, true);
 
         RLController<PlayerAction> rl = new RLControllerGUI<>(
                 new BlackJackTable(),

src/main/java/example/ContinuousAnt.java

@@ -1,26 +0,0 @@
-package example;
-
-import core.RNG;
-import core.algo.Method;
-import core.controller.RLController;
-import core.controller.RLControllerGUI;
-import evironment.antGame.AntAction;
-import evironment.antGame.AntWorldContinuous;
-import evironment.antGame.AntWorldContinuousOriginalState;
-
-public class ContinuousAnt {
-    public static void main(String[] args) {
-        RNG.setSeed(56);
-        RLController<AntAction> rl = new RLControllerGUI<>(
-                new AntWorldContinuousOriginalState(8, 8),
-                Method.Q_LEARNING_OFF_POLICY_CONTROL,
-                AntAction.values());
-
-        rl.setDelay(200);
-        rl.setNrOfEpisodes(10000);
-        rl.setDiscountFactor(0.95f);
-        rl.setEpsilon(0.15f);
-
-        rl.start();
-    }
-}

src/main/java/example/DinoSampling.java

@@ -3,9 +3,7 @@ package example;
 import core.RNG;
 import core.algo.Method;
 import core.controller.RLController;
-import core.controller.RLControllerGUI;
 import evironment.jumpingDino.DinoAction;
-import evironment.jumpingDino.DinoWorld;
 import evironment.jumpingDino.DinoWorldAdvanced;
 
 import java.io.File;
@@ -15,8 +13,7 @@ import java.nio.file.Path;
 import java.nio.file.StandardOpenOption;
 
 public class DinoSampling {
-    public static final float f =0.05f;
-    public static final String FILE_NAME = "converge.txt";
+    public static final String FILE_NAME = "advancedEveryVisit.txt";
     public static void main(String[] args) {
         File file = new File(FILE_NAME);
         try {
@@ -24,24 +21,24 @@ public class DinoSampling {
         } catch (IOException e) {
             e.printStackTrace();
         }
-        for(float f = 0.05f; f <=1.003 ; f+=0.05f) {
+        for(float f = 0.05f; f <= 1.003; f += 0.05f) {
             try {
                 Files.writeString(Path.of(file.getPath()), f + ",", StandardOpenOption.APPEND);
             } catch (IOException e) {
                 e.printStackTrace();
             }
-            for (int i = 1; i <= 100; i++) {
+            for(int i = 1; i <= 100; i++) {
                 System.out.println("seed: " + i * 13);
-                RNG.setSeed(i * 13);
+                RNG.setSeed(i * 13, true);
 
                 RLController<DinoAction> rl = new RLController<>(
                         new DinoWorldAdvanced(),
-                        Method.Q_LEARNING_OFF_POLICY_CONTROL,
+                        Method.MC_CONTROL_EVERY_VISIT,
                         DinoAction.values());
                 rl.setDelay(0);
-                rl.setDiscountFactor(0.99f);
+                rl.setDiscountFactor(1f);
                 rl.setEpsilon(f);
-                rl.setLearningRate(0.9f);
+                rl.setLearningRate(1f);
                 rl.setNrOfEpisodes(400000);
                 rl.start();
             }

src/main/java/example/JumpingDino.java

@@ -4,12 +4,11 @@ import core.RNG;
 import core.algo.Method;
 import core.controller.RLController;
 import evironment.jumpingDino.DinoAction;
-import evironment.jumpingDino.DinoWorld;
 import evironment.jumpingDino.DinoWorldAdvanced;
 
 public class JumpingDino {
     public static void main(String[] args) {
-        RNG.setSeed(29);
+        RNG.setSeed(29, true);
 
         RLController<DinoAction> rl = new RLController<>(
                 new DinoWorldAdvanced(),

src/main/java/example/Results

@@ -1,15 +0,0 @@
-Method:
-Epsilon = k / currentEpisode
-set to 0 if Epsilon < b
-
-k = 1.5
-b = 0.1   => conv. 16
-
-k = 1.5
-b = 0.02  => 75
-
-k = 1.4
-b = 0.02  => fail
-
-k = 2.0
-b = 0.02  => conv. 100

src/main/java/example/RunningAnt.java

@@ -9,7 +9,7 @@ import evironment.antGame.AntWorld;
 
 public class RunningAnt {
     public static void main(String[] args) {
-        RNG.setSeed(56);
+        RNG.setSeed(56, true);
 
         RLController<AntAction> rl = new RLControllerGUI<>(
                 new AntWorld(8, 8),
@@ -20,7 +20,6 @@ public class RunningAnt {
         rl.setNrOfEpisodes(10000);
         rl.setDiscountFactor(0.9f);
         rl.setEpsilon(0.15f);
-
         rl.start();
     }
 }

src/main/java/example/Test.java

@@ -1,52 +0,0 @@
-package example;
-
-public class Test {
-    interface Drawable{
-        void draw();
-    }
-    interface State{
-        int getInt();
-    }
-
-    static class A implements  Drawable, State{
-        private int k;
-        public A(int a){
-            k = a;
-        }
-        @Override
-        public void draw() {
-            System.out.println("draw " + k);
-        }
-
-        @Override
-        public int getInt() {
-            System.out.println("getInt" + k);
-            return k;
-        }
-    }
-
-    static class B  implements State{
-        @Override
-        public int getInt() {
-            return 0;
-        }
-    }
-
-    public static void main(String[] args) {
-        State state = new A(24);
-        State state2 = new B();
-        state.getInt();
-
-        System.out.println(state2 instanceof Drawable);
-        drawState(state2);
-    }
-
-    static void drawState(State s){
-        if(s instanceof Drawable){
-            Drawable d = (Drawable) s;
-            d.draw();
-        }else{
-            System.out.println("invalid");
-        }
-    }
-}

src/test/java/MCFirstVisit.java

@@ -1,30 +0,0 @@
-import core.RNG;
-import core.algo.Method;
-import core.controller.RLController;
-import core.controller.RLControllerGUI;
-import evironment.jumpingDino.DinoAction;
-import evironment.jumpingDino.DinoWorld;
-import org.junit.Test;
-
-public class MCFirstVisit {
-
-    /**
-     * Test if the action sequence is deterministic
-     */
-    @Test
-    public void deterministicActionSequence(){
-        RNG.setSeed(55);
-
-        RLController<DinoAction> rl = new RLControllerGUI<>(
-                new DinoWorld(false, false),
-                Method.MC_CONTROL_FIRST_VISIT,
-                DinoAction.values());
-
-        rl.setDelay(10);
-        rl.setDiscountFactor(1f);
-        rl.setEpsilon(0.1f);
-        rl.setLearningRate(0.8f);
-        rl.setNrOfEpisodes(4000000);
-        rl.start();
-    }
-}