raylab 0.14.3

Reinforcement learning algorithms in RLlib and PyTorch.

Reinforcement learning algorithms in RLlib and PyTorch.

Introduction

Raylab provides agents and environments to be used with a normal RLlib/Tune setup.

import ray
from ray import tune
import raylab

def main():
    raylab.register_all_agents()
    raylab.register_all_environments()
    ray.init()
    tune.run(
        "NAF",
        local_dir=...,
        stop={"timesteps_total": 100000},
        config={
            "env": "CartPoleSwingUp-v0",
            "exploration_config": {
                "type": tune.grid_search([
                    "raylab.utils.exploration.GaussianNoise",
                    "raylab.utils.exploration.ParameterNoise"
                ])
            }
            ...
        },
    )

if __name__ == "__main__":
    main()

One can then visualize the results using raylab dashboard

Installation

pip install raylab

Algorithms

Paper	Agent Name
Actor Critic using Kronecker-factored Trust Region	ACKTR
Trust Region Policy Optimization	TRPO
Normalized Advantage Function	NAF
Stochastic Value Gradients	SVG(inf)/SVG(1)/SoftSVG
Soft Actor-Critic	SoftAC
Streamlined Off-Policy (DDPG)	SOP
Model-Based Policy Optimization	MBPO
Model-based Action-Gradient-Estimator	MAGE

Command-line interface

For a high-level description of the available utilities, run raylab --help

Usage: raylab [OPTIONS] COMMAND [ARGS]...

  RayLab: Reinforcement learning algorithms in RLlib.

Options:
  --help  Show this message and exit.

Commands:
  dashboard    Launch the experiment dashboard to monitor training progress.
  episodes     Launch the episode dashboard to monitor state and action...
  experiment   Launch a Tune experiment from a config file.
  find-best    Find the best experiment checkpoint as measured by a metric.
  info         View information about an agent's config parameters.
  rollout      Wrap `rllib rollout` with customized options.
  test-module  Launch dashboard to test generative models from a checkpoint.

Packages

The project is structured as follows

raylab
|-- agents            # Trainer and Policy classes
|-- cli               # Command line utilities
|-- envs              # Gym environment registry and utilities
|-- logger            # Tune loggers
|-- policy            # Extensions and customizations of RLlib's policy API
|   |-- losses        # RL loss functions
|   |-- modules       # PyTorch neural network modules for TorchPolicy
|-- pytorch           # PyTorch extensions
|-- utils             # miscellaneous utilities