Deep Reinforcement Learning Algorithms with PyTorch

September 11, 2019

Credits: Petros Christodoulou 

 

 

 

Algorithms Implemented
  1. Deep Q Learning (DQN) (Mnih et al. 2013)

  2. DQN with Fixed Q Targets (Mnih et al. 2013)

  3. Double DQN (DDQN) (Hado van Hasselt et al. 2015)

  4. DDQN with Prioritised Experience Replay (Schaul et al. 2016)

  5. Dueling DDQN (Wang et al. 2016)

  6. REINFORCE (Williams et al. 1992)

  7. Deep Deterministic Policy Gradients (DDPG) (Lillicrap et al. 2016 )

  8. Twin Delayed Deep Deterministic Policy Gradients (TD3) (Fujimoto et al. 2018)

  9. Soft Actor-Critic (SAC) (Haarnoja et al. 2018)

  10. Soft Actor-Critic for Discrete Actions (SAC-Discrete) (Christodoulou 2019)

  11. Asynchronous Advantage Actor Critic (A3C) (Mnih et al. 2016)

  12. Syncrhonous Advantage Actor Critic (A2C)

  13. Proximal Policy Optimisation (PPO) (Schulman et al. 2017)

  14. DQN with Hindsight Experience Replay (DQN-HER) (Andrychowicz et al. 2018)

  15. DDPG with Hindsight Experience Replay (DDPG-HER) (Andrychowicz et al. 2018 )

  16. Hierarchical-DQN (h-DQN) (Kulkarni et al. 2016)

  17. Stochastic NNs for Hierarchical Reinforcement Learning (SNN-HRL) (Florensa et al. 2017)

  18. Diversity Is All You Need (DIAYN) (Eyensbach et al. 2018)

 

 

Find the code here: https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

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