Standard 5 stops to get here

Model-Based RL

Reinforcement learning using learned environment models for planning and improving sample efficiency.

Your route here

5 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Reinforcement Learning ✓ understood

    Learning through interaction with an environment, receiving rewards or penalties to learn optimal behavior policies.

  3. Agent ✓ understood

    In RL, the learner or decision-maker that takes actions in an environment to maximize cumulative reward.

  4. Environment ✓ understood

    In RL, the world the agent interacts with, providing states, accepting actions, and returning rewards.

  5. World Model ✓ understood

    A learned model of environment dynamics that can predict future states, used in model-based RL.

  6. Model-Based RL · you are here ✓ understood

Reinforcement learning using learned environment models for planning and improving sample efficiency.

This concept is essential for understanding emerging & advanced and forms a key part of modern AI systems.

  • Reinforcement Learning
  • World Model
  • Planning

Where it sits

Before this

Reinforcement Learning World Model
Model-Based RL

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Agents & RL Model-Free RL Reinforcement learning directly learning policies or value functions without modeling environment dynamics. Agents & RL Monte Carlo Tree Search A search algorithm combining tree search with random sampling, used in game-playing AIs. Agents & RL Deep Q-Network Combining Q-learning with deep neural networks to handle high-dimensional state spaces, enabling RL for complex tasks like Atari games. Agents & RL Hierarchical RL Learning policies at multiple levels of abstraction, with high-level goals and low-level skills. Agents & RL Offline RL Learning policies from fixed datasets without environment interaction, enabling learning from logs.