Deep Deterministic Policy Gradients

Pytorch implementation of the Deep Deterministic Policy Gradients Algorithm for Continuous Control as described by the paper Continuous control with deep reinforcement learning by Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra.

Results

BipedalWalker-V3

Environment Link: https://gym.openai.com/envs/BipedalWalker-v2/

Mean Reward: 169.5047038212551 sampled over 20 evaluation episodes.

Experiment Conducted on Free-P5000 instance provided by Paperspace Gradient.

LunarLanderContinuous-V2

Mean Environment Link: https://gym.openai.com/envs/LunarLanderContinuous-v2/

Reward: 277.938417002226 sampled over 20 evaluation episodes.

Experiment Conducted on Free-P5000 instance provided by Paperspace Gradient.

Reference

@misc{1509.02971,
    Author = {Timothy P. Lillicrap and Jonathan J. Hunt and Alexander Pritzel and Nicolas Heess and Tom Erez and Yuval Tassa and David Silver and Daan Wierstra},
    Title = {Continuous control with deep reinforcement learning},
    Year = {2015},
    Eprint = {arXiv:1509.02971},
}