torchwm 0.3.1

A Modular Pytorch Based library for training world models

TorchWM

TorchWM is a modular PyTorch library for world models and latent dynamics learning. It includes practical implementations of Dreamer-style agents, PlaNet/RSSM utilities, JEPA-style representation learning, and diffusion/transformer building blocks.

Highlights

• Modular components for encoders, decoders, RSSMs, reward/value heads, and policies
• Modular RSSM with swappable encoder/decoder/backbone for research experiments
• Multiple environment backends: DMC, Gym/Gymnasium, Atari, MuJoCo, Unity ML-Agents
• Replay/memory utilities for both Dreamer and PlaNet-style training loops
• ViT + masking utilities for JEPA workflows
• Diffusion utilities (DDPM schedule + DiT model)

Installation

Install from PyPI:

pip install torchwm

Install from source:

git clone https://github.com/ParamThakkar123/torchwm.git
cd torchwm
pip install -e .

Development extras:

pip install -e ".[dev]"

Quick Start

Train Dreamer on Gym

from world_models.models import DreamerAgent
from world_models.configs import DreamerConfig

cfg = DreamerConfig()
cfg.env_backend = "gym"  # dmc | gym | unity_mlagents
cfg.env = "Pendulum-v1"
cfg.total_steps = 10_000

agent = DreamerAgent(cfg)
agent.train()

Train Dreamer on Unity ML-Agents

from world_models.models import DreamerAgent
from world_models.configs import DreamerConfig

cfg = DreamerConfig()
cfg.env_backend = "unity_mlagents"
cfg.unity_file_name = r"E:\UnityBuilds\MyEnv.exe"
cfg.unity_behavior_name = "MyBehavior"
cfg.unity_no_graphics = True
cfg.unity_time_scale = 20.0

agent = DreamerAgent(cfg)
agent.train()

Train JEPA

from world_models.models import JEPAAgent
from world_models.configs import JEPAConfig

cfg = JEPAConfig()
cfg.dataset = "imagefolder"
cfg.root_path = "./data"
cfg.image_folder = "train"
cfg.epochs = 10

agent = JEPAAgent(cfg)
agent.train()

Documentation

• Sphinx source: docs/source
• Getting started guide: docs/source/getting_started.md
• Package overview: docs/source/package_overview.md
• API reference (autodoc): docs/source/api_reference.rst

Build HTML docs locally:

sphinx-build -b html docs/source docs/build/html

Package Layout

• world_models/models: Agents and model architectures (Dreamer, JEPAAgent, Planet, ViT, diffusion)
• world_models/models/modular_rssm: Modular RSSM with swappable encoder/decoder/backbone
• world_models/configs: Config classes (DreamerConfig, JEPAConfig, DiTConfig)
• world_models/envs: Environment adapters and wrappers
• world_models/training: Script-style training entrypoints
• world_models/datasets: CIFAR10/ImageNet/ImageFolder data loaders
• world_models/memory: Replay and episodic memory implementations
• world_models/utils: Training/logging/distributed helper utilities

Contributing

Contributions are welcome. See CONTRIBUTING.md.

License

MIT. See LICENSE.

Citation

@misc{Thakkar_GitHub_-_ParamThakkar123_torchwm,
author = {Thakkar, Param},
title = {{GitHub - ParamThakkar123/torchwm: A modular PyTorch library designed for learning, training, and deploying world models across various environments.}},
year = {2025},
url = {https://github.com/ParamThakkar123/torchwm}
}

Package: torchwm on PyPI