danijar / embodied

Fast reinforcement learning research

 

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:
  __init__(obs_space, act_space, config)
  policy(obs, carry, mode='train') -> act, carry
  train(data, carry) -> metrics, carry
  report(data, carry) -> metrics, carry
  init_policy(batch_size) -> carry
  init_train(batch_size) -> carry
  init_report(batch_size) -> carry
  dataset(generator) -> generator

Env API

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