Implementation of Reinforcement Learning Algorithms in Python

Implementation of selected reinforcement learning algorithms with tensorflow.

Implemented Algorithms

(Click into the links for more details)

Advanced

• Asynchronized Advantage Actor-Critic (A3C)
• Deep Deterministic Policy Gradient (DDPG)

Policy Gradient Methods

• REINFORCE with policy function approximation
• REINFORCE with baseline

Temporal Difference Learning

• Standard epsilon greedy Q-learning
• Deep Q-learning

Monte Carlo Methods

• Monte Carlo (MC) estimation of action values

Dynamic Programming MDP Solver

• Value iteration
• Policy iteration - policy evaluation & policy improvement

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