Standard 2 stops to get here

Model-Free RL

Reinforcement learning directly learning policies or value functions without modeling environment dynamics.

Your route here

2 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. Model-Free RL · you are here ✓ understood

Reinforcement learning directly learning policies or value functions without modeling environment dynamics.

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

  • Reinforcement Learning
  • Q-Learning
  • Policy Gradient

Where it sits

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Reinforcement Learning
Model-Free RL

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Nothing yet: a destination in its own right.

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Agents & RL Model-Based RL Reinforcement learning using learned environment models for planning and improving sample efficiency. Agents & RL Q-Learning A model-free RL algorithm that learns action-value functions (Q-values) to determine optimal actions in each state. Agents & RL Policy Gradient RL methods that directly optimize the policy by computing gradients of expected reward with respect to policy parameters. 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 Actor-Critic RL architecture with two components: an actor (policy) that selects actions and a critic (value function) that evaluates them.