What are the key components of an RL system?

A reinforcement learning (RL) system consists of four core components: the agent, environment, actions and states, and reward function. The agent is the decision-maker that interacts with the environment by taking actions, which transition the system between states. The environment provides feedback through rewards, guiding the agent toward desired behaviors. These elements work together to enable the agent to learn a policy—a strategy for choosing actions—that maximizes cumulative rewards over time. Additional components like a value function (to estimate long-term rewards) or a model (to predict environment dynamics) may also be included, depending on the algorithm.

The first key component is the agent-environment loop. The agent observes the current state (e.g., a robot’s position in a maze or a game board’s configuration) and selects an action (e.g., moving left or placing a game piece). The environment processes the action, updates the state, and returns a reward (e.g., +1 for reaching a goal, -1 for hitting an obstacle). For example, in an inventory management system, the agent might adjust stock levels (action) based on current demand (state) to maximize profit (reward). This loop is repeated continuously, allowing the agent to learn from trial and error.

The second component is the policy, which defines the agent’s behavior. A policy maps states to actions, often represented as a neural network in deep RL or a lookup table in simpler cases. For instance, a chess-playing agent’s policy might prioritize capturing pieces (action) in certain board configurations (state). The value function complements the policy by estimating the expected long-term reward of a state or action, helping the agent balance immediate and future gains. Some systems also include a model of the environment to simulate outcomes without direct interaction, enabling planning (e.g., predicting customer demand in a supply chain). Together, these components create a framework for the agent to learn adaptive strategies through iterative feedback.