Model Based Algorithms and World Models

Key Concepts

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Model-based reinforcement learning algorithms learn a model of the environment dynamics and use it for planning, often achieving better sample efficiency than model-free methods.

Key Concepts

World Models: Neural networks that predict future states
Model Predictive Control (MPC): Planning with learned dynamics
Dyna Architecture: Combining real and simulated experience
Latent Space Models: Learning compressed state representations

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Introduction to RL

Monte-Carlo Prediction

⌘I

Reinforcement Learning

Model-Based

Prediction

Control

Policy-Based

Model Based Algorithms and World Models

Key Concepts

Reinforcement Learning

Model-Based

Prediction

Control

Policy-Based

​Key Concepts

Key Concepts