 Jan Löwenstrom
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7d3d097599
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add opening dialog to select all learning settings
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2020-04-07 11:03:17 +02:00 |
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Jan Löwenstrom
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740289ee2b
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add constant for default reward
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2020-04-02 14:01:37 +02:00 |
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Jan Löwenstrom
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eca0d8db4d
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create Dino Sampling state
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2020-03-26 19:22:50 +01:00 |
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Jan Löwenstrom
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ee1d62842d
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split Antworld into episodic and continuous task
- add new simple state for jumping dino, to see if convergence is guarenteed with with state representation
- changed reward structure for ant game
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2020-03-15 16:58:53 +01:00 |
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Jan Löwenstrom
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6613e23c7c
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Fixed new method name for MC
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2020-03-02 23:19:54 +01:00 |
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Jan Löwenstrom
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77898f4e5a
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add TD algorithms and started adopting to continous tasks
- add Q-Learning and SARSA
- more config variables
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2020-02-17 13:56:55 +01:00 |
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Jan Löwenstrom
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518683b676
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split GUI parts from controller into sub class
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2019-12-31 14:43:40 +01:00 |
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Jan Löwenstrom
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e0160ca1df
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adopt MVC pattern and add real time graph interface
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2019-12-18 16:48:24 +01:00 |
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Jan Löwenstrom
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584d6a1246
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add javaFX gradle plugin and switch to java11 and add system.outs for error detecting
- The current implementation will not converge to the correct behaviour. See comment in MonteCarlo class for more details
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2019-12-10 15:37:20 +01:00 |
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Jan Löwenstrom
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0100f2e82a
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remove the Action interface in favour of Enums
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2019-12-09 17:30:14 +01:00 |
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Jan Löwenstrom
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2fb218a129
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add separate class for intern Ant representation and adopt gui cell size to panel size
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2019-12-09 12:08:53 +01:00 |
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Jan Löwenstrom
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db9b62236c
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add logic to handle ant action and compute rewards
- ant world will handle and compute action received by the agent
- first try to convert observations to markov states
- improved .equals() methods
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2019-12-08 16:03:00 +01:00 |
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Jan Löwenstrom
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87f435c65a
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add basic core structure and first parts of antGame implementation
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2019-12-07 22:05:11 +01:00 |
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Jan Löwenstrom
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66ee33b77f
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init the gradle project
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2019-12-06 13:11:29 +01:00 |