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Implementation of the Deep Deterministic Policy Gradient (DDPG) using PyTorch

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Deep Deterministic Policy Gradient on PyTorch

Overview

The is the implementation of Deep Deterministic Policy Gradient (DDPG) using PyTorch. Part of the utilities functions such as replay buffer and random process are from keras-rl repo. Contributes are very welcome.

Dependencies

Run

  • Training : results of two environment and their training curves:

    • Pendulum-v0
    $ ./main.py --debug
    alternate text
    • MountainCarContinuous-v0
    $ ./main.py --env MountainCarContinuous-v0 --validate_episodes 100 --max_episode_length 2500 --ou_sigma 0.5 --debug
    alternate text
  • Testing :

$ ./main.py --mode test --debug

TODO

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Implementation of the Deep Deterministic Policy Gradient (DDPG) using PyTorch

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