danijar/dreamerv2

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1056 stars · 212 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A TensorFlow 2 implementation of the DreamerV2 model-based reinforcement learning agent that learns world models from high-dimensional images and achieves human-level Atari performance. It is installable via pip and trains on a single GPU across discrete and continuous action environments.

Use cases

• train a reinforcement learning agent on Atari games
• learn a world model from image observations
• run model-based RL on Gym environments like MiniGrid
• reproduce DreamerV2 research results
• compare model-based agents against Rainbow and IQN
• train RL agents with continuous action spaces

When to choose

• you need a proven model-based RL agent with human-level Atari performance
• you want to train from high-dimensional image inputs on a single GPU
• you need a pip-installable RL library supporting discrete and continuous actions
• you are doing research on world models or video prediction

When to avoid

• you need a simple model-free baseline like DQN or PPO
• you require PyTorch instead of TensorFlow 2
• you need production RL deployment rather than research experimentation
• you need actively maintained code with recent updates

Facets

library · maturity stable

reinforcement-learning machine-learning simulation reinforcement-learning artificial-intelligence deep-learning robotics python world-models atari model-based-rl tensorflow research-code video-prediction gpu linux

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markdown · JSON · MCP: product_card(name="danijar/dreamerv2")