A policy in reinforcement learning (RL) is a strategy or a mapping that determines the agent’s actions based on its current state. It defines the agent’s behavior by specifying which action to take in a given state. Policies can be deterministic (always choosing the same action for a given state) or stochastic (choosing actions based on a probability distribution).
The policy guides the agent throughout the learning process and dictates how it interacts with the environment. The goal is for the agent to learn an optimal policy, one that maximizes cumulative rewards over time. For instance, a policy might dictate that a robot should always move forward unless an obstacle is detected, at which point it should turn.
In practice, the policy can be represented as a function or a table (in the case of small environments) that maps states to actions. In larger, more complex environments, the policy might be learned through deep learning methods, where a neural network is used to approximate the optimal actions.