yrlu/reinforcement_learning

Implementation of selected reinforcement learning algorithms in Tensorflow. A3C, DDPG, REINFORCE, DQN, etc.

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