Reference 4 stops to get here

Hierarchical RL

Learning policies at multiple levels of abstraction, with high-level goals and low-level skills.

Your route here

4 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. Policy ✓ understood

    A strategy or mapping from states to actions that defines the agent's behavior in reinforcement learning.

  5. Hierarchical RL · you are here ✓ understood

Learning policies at multiple levels of abstraction, with high-level goals and low-level skills.

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

  • Reinforcement Learning
  • Temporal Abstraction
  • Options

Where it sits

Before this

Reinforcement Learning Policy
Hierarchical RL

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Agents & RL Meta-RL Learning to adapt quickly to new RL tasks from experience on related tasks. Agents & RL Model-Based RL Reinforcement learning using learned environment models for planning and improving sample efficiency. Agents & RL Multi-Agent RL Reinforcement learning with multiple agents that interact and potentially cooperate or compete. Agents & RL World Model A learned model of environment dynamics that can predict future states, used in model-based RL.