MatthewGerber/rlai
This is a Python implementation of concepts and algorithms described in "Reinforcement Learning: An Introduction" (Sutton and Barto, 2018, 2nd edition).
PythonMIT
Issues
- 1
Revisit Swimmer-v4
#61 opened by MatthewGerber - 0
Add mypy
#52 opened by MatthewGerber - 1
- 1
- 0
Rerun continuous mountain car
#57 opened by MatthewGerber - 0
- 0
Rerun swimming worm
#56 opened by MatthewGerber - 0
Tick label doesn't show up in state/reward scatter plot for cartpole run.
#59 opened by MatthewGerber - 0
Remove non-stationary feature scaling
#48 opened by MatthewGerber - 0
Use state-dimension segments
#49 opened by MatthewGerber - 0
- 0
Revive Jupyter Notebook
#54 opened by MatthewGerber - 0
Fix robocode tests
#53 opened by MatthewGerber - 0
Reconcile td alpha with function approximation step size (currently ignored)
#15 opened by MatthewGerber - 0
Get coefficient boxplots to work for actions with varying numbers of features
#28 opened by MatthewGerber - 0
- 0
Remove epsilon passed to GPI functions
#20 opened by MatthewGerber - 0
- 0
Increase test coverage
#9 opened by MatthewGerber - 0
- 0
Refactor code to gather up environments, states, and extractors per environment.
#14 opened by MatthewGerber - 0
- 0
Refactor entry point to be rlai with sub-commands for train, agent in environment, etc.
#11 opened by MatthewGerber - 0
Chain argument parsers to display help
#10 opened by MatthewGerber - 0
Example/test of state-action interaction feature extractor on command line
#6 opened by MatthewGerber - 0