What is a policy in RL?

In reinforcement learning (RL), a policy defines how an agent decides which actions to take in different situations. It is essentially a set of rules or a strategy that maps the agent’s current state (its observation of the environment) to an action. The policy can be deterministic, where a specific state always leads to the same action, or stochastic, where the policy outputs probabilities for each possible action. For example, in a gridworld game where an agent navigates a maze, a deterministic policy might always move the agent left when in a specific cell, while a stochastic policy might assign a 70% chance to move left and a 30% chance to move up. The policy is the core component that shapes the agent’s behavior, and improving it is the primary goal of most RL algorithms.

Policies are learned through interaction with the environment. During training, the agent experiments with actions, observes rewards (feedback), and adjusts its policy to maximize cumulative rewards over time. For instance, in Q-learning, the agent builds a table (Q-table) that estimates the expected reward for each state-action pair. The policy here might be to always choose the action with the highest Q-value (greedy policy). In contrast, policy gradient methods directly optimize the policy by adjusting its parameters using gradient ascent on the expected reward. A practical example is training a robot to walk: the policy could be a neural network that takes sensor data as input and outputs joint torque values. The network’s parameters are updated to increase the likelihood of actions that led to successful movement.

The design of the policy significantly impacts the agent’s performance and learning efficiency. Simple policies, like lookup tables, work for small state spaces but fail in complex environments like video games or autonomous driving. Here, neural networks are often used as function approximators to generalize across states. Policies also balance exploration (trying new actions) and exploitation (using known effective actions). For example, an epsilon-greedy policy in Q-learning randomly explores with probability epsilon while exploiting the best-known action otherwise. A poorly designed policy might get stuck in suboptimal behaviors, while a well-tuned one adapts to dynamic environments. Ultimately, the policy encapsulates the agent’s decision-making logic, making it a critical focus in RL system design.