google-research/planet

Health v2 · maintenance only

Flags: no_releases archived

Adoption not part of the score

1260 stars · 218 forks observed · 2026-08-28

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

Open-source implementation of the PlaNet agent, a purely model-based reinforcement learning algorithm that solves control tasks from pixels by planning in a learned latent space. It encodes image histories into compact latent states and predicts future rewards for candidate action sequences to act efficiently with minimal environment interaction.

Use cases

• train a model-based RL agent on control tasks from images
• reproduce PlaNet results from the ICML 2019 paper
• experiment with latent dynamics models for planning
• benchmark model-based vs model-free RL sample efficiency
• modify latent transition models for RL research
• run RL experiments on DeepMind Control Suite tasks like cheetah_run

When to choose

• you need a model-based RL baseline that learns from pixels with low sample complexity
• you want to study or extend latent dynamics world models for control
• you are reproducing or building on the PlaNet paper

When to avoid

• you need a production-ready or actively maintained RL framework
• you want model-free RL algorithms like SAC or PPO
• you need a simple high-level API rather than research code

Facets

library · maturity maintenance

reinforcement-learning machine-learning simulation reinforcement-learning machine-learning artificial-intelligence python model-based-rl latent-dynamics planning research-code deep-learning linux gpu

Member repositories

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markdown · JSON · MCP: product_card(name="google-research/planet")