SLM Lab
Modular Deep Reinforcement Learning framework in PyTorch.
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Modular Deep Reinforcement Learning framework in PyTorch.
Last updated
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SLM Lab is a software framework for reproducible reinforcement learning (RL) research. It enables easy development of RL algorithms using modular components and file-based configuration. It also enables flexible experimentation completed with hyperparameter search, result analysis and benchmark results.
SLM Lab is also the companion library of the book . The book's .
for building deep RL algorithms
using spec file and git SHA
with
Well-tuned algorithm implementations
Multiple RL environment offerings
SLM Lab implements most of the :
SARSA
DQN (Deep Q-Network)
Double-DQN, Dueling-DQN, PER (Prioritized Experience Replay)
REINFORCE
A2C (Advantage Actor-Critic) with GAE & n-step
PPO (Proximal Policy Optimization)
SAC (Soft Actor-Critic)
SIL (Self Imitation Learning)
Asynchronous version of all the above
They are implemented in a modular way such that differences in algorithm performance can be confidently ascribed to differences between algorithms, not between implementations.
SLM Lab currently includes the following environment offerings:
If you use SLM Lab in your publication, please cite below:
(credit: joelouismarino)
with prebuilt binaries
This project is licensed under the .
