Standard 6 stops to get here · leads to 1

Q-Learning

A model-free RL algorithm that learns action-value functions (Q-values) to determine optimal actions in each state.

Your route here

6 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. Reward ✓ understood

    The single number an environment sends back after each action, telling a reinforcement learning agent how good that step was; the agent learns to maximize its long-run total.

  4. Agent ✓ understood

    In reinforcement learning, the learner and decision-maker that observes an environment, takes actions and adjusts its behaviour to maximize cumulative reward.

  5. Policy ✓ understood

    The rule an RL agent uses to choose actions: a mapping from each state to an action, or to a probability distribution over actions.

  6. Value Function ✓ understood

    A function estimating expected cumulative reward from a state (state-value) or state-action pair (action-value/Q-value).

  7. Q-Learning · you are here ✓ understood

Where it sits

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Value Function
Q-Learning

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Deep Q-Network

Explore nearby

Agents & RL Bellman Equation The recursive rule at the heart of reinforcement learning: a state's value equals the reward you get now plus the discounted value of where you land next. 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 Policy Gradient RL methods that directly optimize the policy by computing gradients of expected reward with respect to policy parameters. Agents & RL Model-Free RL Reinforcement learning directly learning policies or value functions without modeling environment dynamics. Agents & RL Exploration vs Exploitation The RL dilemma of trying new actions (exploration) versus using known good actions (exploitation) to maximize reward. Agents & RL Offline RL Learning policies from fixed datasets without environment interaction, enabling learning from logs.

In the research

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A paper that builds on Q-Learning .