raylab 0.16.0

Reinforcement learning algorithms in RLlib and PyTorch.

Reinforcement learning algorithms in RLlib and PyTorch.

Installation

pip install raylab

Quickstart

Raylab provides agents and environments to be used with a normal RLlib/Tune setup. You can an agent’s name (from the Algorithms section) to raylab info list to list its top-level configurations:

raylab info list SoftAC

learning_starts: 0
    Hold this number of timesteps before first training operation.
policy: {}
    Sub-configurations for the policy class.
wandb: {}
    Configs for integration with Weights & Biases.

    Accepts arbitrary keyword arguments to pass to `wandb.init`.
    The defaults for `wandb.init` are:
    * name: `_name` property of the trainer.
    * config: full `config` attribute of the trainer
    * config_exclude_keys: `wandb` and `callbacks` configs
    * reinit: True

    Don't forget to:
      * install `wandb` via pip
      * login to W&B with the appropriate API key for your
        team/project.
      * set the `wandb/project` name in the config dict

    Check out the Quickstart for more information:
    `https://docs.wandb.com/quickstart`

You can add the --rllib flag to get the descriptions for all the options common to RLlib agents (or Trainers)

Launching experiments can be done via the command line using raylab experiment passing a file path with an agent’s configuration through the --config flag. The following command uses the cartpole example configuration file to launch an experiment using the vanilla Policy Gradient agent from the RLlib library.

raylab experiment PG --name PG -s training_iteration 10 --config examples/PG/cartpole_defaults.py

You can also launch an experiment from a Python script normally using Ray and Tune. The following shows how you may use Raylab to perform an experiment comparing different types of exploration for the NAF agent.

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="data/NAF",
        stop={"timesteps_total": 100000},
        config={
            "env": "CartPoleSwingUp-v0",
            "exploration_config": {
                "type": tune.grid_search([
                    "raylab.utils.exploration.GaussianNoise",
                    "raylab.utils.exploration.ParameterNoise"
                ])
            }
        },
        num_samples=10,
    )

if __name__ == "__main__":
    main()

One can then visualize the results using raylab dashboard, passing the local_dir used in the experiment. The dashboard lets you filter and group results in a quick way.

raylab dashboard data/NAF/

You can find the best checkpoint according to a metric (episode_reward_mean by default) using raylab find-best.

raylab find-best data/NAF/

Finally, you can pass a checkpoint to raylab rollout to see the returns collected by the agent and render it if the environment supports a visual render() method. For example, you can use the output of the find-best command to see the best agent in action.

raylab rollout $(raylab find-best data/NAF/) --agent NAF

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