A policy in reinforcement learning (RL) is a strategy or set of rules that an agent uses to decide which actions to take in different states of an environment. At its core, a policy defines the agent’s behavior: it maps observations (states) to actions, guiding the agent on what to do in any given situation. For example, in a game like chess, a policy might determine which piece to move based on the current board configuration. Policies can be simple (like a lookup table) or complex (like a neural network), depending on the problem’s complexity. The ultimate goal of the policy is to maximize the cumulative reward the agent receives over time by making optimal decisions.
Policies can be either deterministic or stochastic. A deterministic policy always selects the same action for a given state, such as a robot following a fixed path in a grid world. In contrast, a stochastic policy assigns probabilities to different actions, allowing the agent to explore and handle uncertainty. For instance, a self-driving car might use a stochastic policy to occasionally test alternative routes in traffic, balancing exploration (trying new actions) with exploitation (using known effective actions). Policies are often updated during training using algorithms like Q-learning, policy gradients, or actor-critic methods. For example, in Q-learning, the agent learns a Q-table that estimates the value of actions in states, and the policy might involve selecting the action with the highest Q-value.
The design of the policy directly impacts how effectively the agent learns. A poorly designed policy might get stuck in suboptimal behaviors, while a well-structured one can adapt to dynamic environments. For example, in a maze-solving task, a policy that prioritizes moving toward the goal while avoiding walls will learn faster than one that randomly wanders. Modern RL frameworks, such as Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO), use neural networks to represent policies, enabling them to handle high-dimensional inputs like images or sensor data. Developers often experiment with policy architectures, exploration strategies, and reward shaping to improve learning efficiency. In summary, the policy is the backbone of an RL agent, defining how it interacts with the environment and evolves through experience.