Implementation of selected reinforcement learning algorithms in Tensorflow. A3C, DDPG, REINFORCE, DQN, etc.
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A3C
DP
DQN
TD
ddpg
envs
imgs
monte_carlo
papers
policy_gradient
.gitignore
LICENSE
README.md
requirements.txt

README.md

Implementations of Reinforcement Learning Algorithms in Python

Implementations of selected reinforcement learning algorithms with tensorflow and openai gym. Mainly for teaching myself RL.

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