Metadata-Version: 2.1
Name: embodied
Version: 1.2.0
Summary: Fast reinforcement learning research
Home-page: http://github.com/danijar/embodied
Author: Danijar Hafner
Author-email: mail@danijar.com
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
Provides-Extra: dreamerv3
License-File: LICENSE

[![PyPI](https://img.shields.io/pypi/v/embodied.svg)](https://pypi.python.org/pypi/embodied/#history) &nbsp;
[![Docs](https://readthedocs.org/projects/embodied/badge)](https://embodied.readthedocs.org)

# 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

```python
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

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