This repo is implements of Reinforcement Learning Algorithms, implementing as learning, some of them are even another version of some tutorial. Any contributions are welcomed.

• Deep Deterministic Policy Gradient (DDPG)
  Implement of DDPG.

  arXiv:1509.02971: Continuous control with deep reinforcement learning

• Asynchronous Advantage Actor-Critic Model (A3C)
  Implement of A3C.

  arXiv:1602.01783: Asynchronous Methods for Deep Reinforcement Learning

• Double-DQN
  Implement of Double-DQN.

  arXiv:1509.06461: Deep Reinforcement Learning with Double Q-learning

• Dueling-DQN
  Implement of Dueling-DQN.

  arXiv:1511.06581: Dueling Network Architectures for Deep Reinforcement Learning

• Deep Q-Network (DQN)
  Implement of DQN.

  arXiv:1312.5602: Playing Atari with Deep Reinforcement Learning

• Actor-Critic Model
  Implement of Actor-Critic Model.

  arXiv:1607.07086: An Actor-Critic Algorithm for Sequence Prediction

• Policy Gradient (PG)
  Implement of Policy Gradient.

  NIPS. Vol. 99. 1999: Policy gradient methods for reinforcement learning with function approximation

• Q-Learning
  Implements of Q-Learning.

  Paper: Convergence of Q-learning: a simple proof

• Sarsa
  Implement of Sarsa.

  Paper: Online Q-Learning using Connectionist Systems

• Python3.5
• TensorFlow1.4
• gym
• numpy
• matplotlib
• pandas (option)

All algorithms are implemented with TensorFlow, the default environment are games provided by gym. You can just clone this project, and run the each algorithm by:

python3.5 algorithms/algo_name.py

• More implements of Deep Reinforcement Learning Paper and Methods.