Work
- Made changes according to comments from last meeting to thesis plan
- Read several more papers
- Research about available frameworks
- RL Glue; supports Java, C/C++, Matlab, Python, Lisp
- Maja Machine Learning Framework; Python
- Reinforcement Learning Toolbox; C++
- Several small environments (mostly in Matlab or Python)
- Personal preference for RL GLue
- Supports my preferred language; Java
- Possibly best support of the framework options above
- Multi-platform / -language
- Used in the last RL Competitions
- Online library with environments, agents and experiments
- Installed RL Glue
- Created Netbeans Java project sourcing all neccesary libraries
- Created helper class to start up RL Viz, RL Glue, agent, environment and experiment
- Changed build.xml to automatically build everything and run (in one click)
- Downloaded RandomAgent, CartPole and MountainCar from rl-library for testing purposes
- Created new environment; one shot - Six-Hump Camel Back
Plans
- Investigate RL Glue environment more
- Finish Six Hump Camel Back One shot environment (visualization)
- Find environments taking continuous actions (not in rl-library; perhaps from last year's RL competitions)
- Change Cart Pole environment to take continuous actions
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