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dreamerv3-torch

Pytorch implementation of Mastering Diverse Domains through World Models. DreamerV3 is a scalable algorithm that outperforms previous approaches across various domains with fixed hyperparameters.

Instructions

Method 1: Manual

Get dependencies with python 3.11:

pip install -r requirements.txt

Run training on DMC Vision:

python3 dreamer.py --configs dmc_vision --task dmc_walker_walk --logdir ./logdir/dmc_walker_walk

Monitor results:

tensorboard --logdir ./logdir

To set up Atari or Minecraft environments, please check the scripts located in env/setup_scripts.

Method 2: Docker

Please refer to the Dockerfile for the instructions, as they are included within.

Benchmarks

So far, the following benchmarks can be used for testing.

Environment	Observation	Action	Budget	Description
DMC Proprio	State	Continuous	500K	DeepMind Control Suite with low-dimensional inputs.
DMC Vision	Image	Continuous	1M	DeepMind Control Suite with high-dimensional images inputs.
Atari 100k	Image	Discrete	400K	26 Atari games.
Crafter	Image	Discrete	1M	Survival environment to evaluates diverse agent abilities.
Minecraft	Image and State	Discrete	100M	Vast 3D open world.
Memory Maze	Image	Discrete	100M	3D mazes to evaluate RL agents' long-term memory.

Results

DMC Proprio

dmcproprio

DMC Vision

dmcvision

Atari 100k

atari100k

Crafter

Acknowledgments

This code is heavily inspired by the following works:

danijar's Dreamer-v3 jax implementation: https://github.com/danijar/dreamerv3
danijar's Dreamer-v2 tensorflow implementation: https://github.com/danijar/dreamerv2
jsikyoon's Dreamer-v2 pytorch implementation: https://github.com/jsikyoon/dreamer-torch
RajGhugare19's Dreamer-v2 pytorch implementation: https://github.com/RajGhugare19/dreamerv2
denisyarats's DrQ-v2 original implementation: https://github.com/facebookresearch/drqv2