A pytorch tutorial for Deep Reinforcement Learning

Deep_RL_with_pytorch

Deep Reinforcement Learning with PyTorch.

• [x] 1. Dynamic Programming (Update : 13. 2. 2019)

  1. Conditional GAN

 2. Policy Iteration & Value Iteration

• [x] 2. Value Based Methods (Update : 17. 2. 2019)

  1. Vanilla DQN

  2. PDD DQN

• [x] 3. Policy Based Methods (Update : 23. 2. 2019)

  1. A2C

  2. PPO

• [x] 4. Off-policy Policy Based Methods (Update : 10. 3. 2019)

  1. SAC

  2. SIL ( not with A2C, PPO but SAC)

• [x] 5. Exploration Techniques (Update : 16. 3. 2019)

  1. Thompson sampling with MCDO
  2. RND

  Breakout with only intrinsic rewards

• [x] 6. Uncertainty in RL (Update : 24. 3. 2019)

  1. Categorical DQN(C51)
  2. QR-DQN
  3. Implicit Quantile Networks

• [x] 7. Imitation Learning (Update : 30. 3. 2019)

  1. GAIL

• [x] 8. Multi-Agent RL (Update : 4. 4. 2019)

  1. Upper Confidence Bounds for Tree(UCT)
  2. Counterfactual Hedge

GitHub