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.
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.
Environments
SLM Lab currently includes the following environment offerings: