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Implementation of selected reinforcement learning algorithms in Tensorflow. A3C, DDPG, REINFORCE, DQN, etc.
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A3C clean up a3c code - pep8 Apr 29, 2017
DP clean up Feb 26, 2017
DQN update dqn May 25, 2017
TD refactor dqn cnn Feb 20, 2017
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imgs papers Feb 17, 2017
monte_carlo repo clean up Feb 27, 2017
papers paper Feb 22, 2017
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README.md Update README.md Apr 13, 2019
requirements.txt

README.md

Implementations of Reinforcement Learning Algorithms in Python

Implementations of selected reinforcement learning algorithms with tensorflow.

Implemented Algorithms

(Click into the links for more details)

Advanced
Policy Gradient Methods
Temporal Difference Learning
Monte Carlo Methods
Dynamic Programming MDP Solver

OpenAI Gym Examples

Environments

  • envs/gridworld.py: minimium gridworld implementation for testings

Dependencies

  • Python 2.7
  • Numpy
  • Tensorflow 0.12.1
  • OpenAI Gym (with Atari) 0.8.0
  • matplotlib (optional)

Tests

  • Files: test_*.py
  • Run unit test for [class]:

python test_[class].py

MIT License

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