What does a policy define in policy-based reinforcement learning?

In policy-based reinforcement learning, a policy defines the strategy that the agent utilizes at each decision step. This means it is a mapping from the set of states the agent could encounter to the actions the agent will take in those states. Essentially, the policy dictates the behavior of the agent, guiding it in selecting actions based on the current situation or state it finds itself in within the environment.

A policy can be deterministic, specifying exactly one action to take in each state, or stochastic, where the policy provides a probability distribution over actions for each state. The effectiveness of an agent's learning directly hinges on how well its policy navigates the decision-making process as it interacts with its environment over time to maximize cumulative rewards.

In contrast to defining the best possible outcome, the policy focuses on the agent's behavior rather than end goals. Additionally, the reward value associated with each state pertains to the reinforcement learning framework but does not describe the agent's strategy directly. The learning rate, on the other hand, is a parameter related to how quickly an agent updates its knowledge but is not the focus of what a policy is defined as. Thus, the central functionality of a policy as a guiding strategy makes the option about the agent's decision-making process the correct choice