Landmark 3 stops to get here · leads to 4

Policy

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

Your route here

3 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 · you are here ✓ understood

Picture it

Observe state sWhat the agent sees right nowPolicy picks action aπ(a | s): a rule or a distributionEnvironment respondsThe world changesReward + next stateFeedback used to improve the…Policy π: state → action
  1. 01 Observe state s What the agent sees right now
  2. 02 Policy picks action a π(a | s): a rule or a distribution
  3. 03 Environment responds The world changes
  4. 04 Reward + next state Feedback used to improve the policy

↺ back to 01 · Policy π: state → action

Notice the policy sits at the decision point of every loop: given the state, it decides the action, and learning reshapes that choice.

Where it sits

Before this

Agent
Policy

Leads to

Policy Gradient Value Function Hierarchical RL Offline RL

Explore nearby

Agents & RL Value Function A function estimating expected cumulative reward from a state (state-value) or state-action pair (action-value/Q-value). Agents & RL Reward A scalar feedback signal indicating how good an action was, used to train reinforcement learning agents. Agents & RL Environment In RL, the world the agent interacts with, providing states, accepting actions, and returning rewards. Agents & RL Policy Gradient RL methods that directly optimize the policy by computing gradients of expected reward with respect to policy parameters. Agents & RL Markov Decision Process A mathematical framework for modeling sequential decision-making with states, actions, rewards, and transition probabilities.

In the research

All papers →

4 papers that build on Policy .