Additions:

• Environment Farming

Bugs:

• Dinamic allocation of Eval_Sampler(environment for it) - FIXED
• Environment allocation bugs - FIXED
• Segmentation fault - FIXED

rlkit

Reinforcement learning framework and algorithms implemented in PyTorch.

Some implemented algorithms:

• Temporal Difference Models (TDMs)
  • example script
  • TDM paper
  • Details on implementation
• Deep Deterministic Policy Gradient (DDPG)
  • example script
  • DDPG paper
• (Double) Deep Q-Network (DQN)
  • example script
  • DQN paper
  • Double Q-learning paper
• Soft Actor Critic (SAC)
  • example script
  • SAC paper
  • TensorFlow implementation from author
• Twin Dueling Deep Determinstic Policy Gradient (TD3)
  • example script
  • TD3 paper

To get started, checkout the example scripts, linked above.

Installation

Install and use the included ananconda environment

$ conda env create -f rlkit-env.yml
$ source activate rlkit-env

Credits

A lot of the coding infrastructure is based on rllab. The serialization and logger code are basically a carbon copy of the rllab versions.