In Reinforcement Learning (RL), a policy is a strategy or a set of rules that defines how an agent behaves in a given environment. It maps the current state of the environment to actions that the agent can take. Essentially, the policy is the function that decides what action to take based on the information available about the state. Depending on the implementation, a policy can be deterministic, meaning it always selects the same action for a given state, or stochastic, meaning it provides a distribution over possible actions, allowing for randomness in decision-making.
There are typically two main types of policies used in RL: learned policies and predefined policies. A learned policy is generated through the training process, where the agent interacts with the environment, receives rewards or penalties, and adjusts its strategy based on these experiences. For example, in a game like Chess, the agent could learn over time which moves yield better results. On the other hand, a predefined policy is established based on expert knowledge about the task or environment. For instance, a basic policy for a navigation task could dictate that the agent always moves toward the goal while avoiding obstacles.
In practice, implementing a policy can involve various approaches. One common method is using a neural network to represent the policy, allowing for complex decision-making in high-dimensional spaces. Techniques like Policy Gradient Methods optimize the policy directly by adjusting its parameters to maximize expected rewards over time. Other methods, such as Q-learning, indirectly derive policies from action-value functions. By understanding the role of policies in RL, developers can create agents that learn to make informed decisions effectively within their environments.