Skip to main content

Fast reinforcement learning research

Project description

PyPI   Docs

Embodied

Fast reinforcement learning research.

Overview

The goal of Embodied is to empower researchers to quickly implement new agents at scale. Embodied achieves this by specifying an interface both for environments and agents, allowing users to mix and match agents, envs, and evaluation protocols. Embodied provides common building blocks that users are encouraged to fork when more control is needed. The only dependency is Numpy and agents can be implemented in any framework.

Packages

embodied/
  core/    # Config, logging, checkpointing, simulation, wrappers
  run/     # Evaluation protocols that combine agents and environments
  envs/    # Environment suites such as Gym, Atari, DMC, Crafter
  agents/  # Agent implementations

Agent API

class Agent:
  @configs -> dict of configs
  __init__(obs_space, act_space, step, config)
  dataset(generator) -> generator
  policy(obs, state=None, mode='train') -> act, state
  train(data, state=None) -> state, metrics
  report(data) -> metrics

Env API

class Env:
  __len__() -> int
  @obs_space -> dict of spaces
  @act_space -> dict of spaces
  step(action) -> obs dict
  render() -> array
  close()

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

embodied-0.3.0.tar.gz (54.2 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page