A reinforcement learning (RL) problem consists of four core components: the agent, the environment, actions, and rewards. The agent is the decision-maker that interacts with the environment by taking actions. The environment represents the world the agent operates in, providing feedback in the form of states and rewards. Actions are the choices the agent makes, which influence the environment’s state. Rewards are numerical signals that guide the agent toward its goal by indicating the immediate value of an action. Additionally, RL problems often involve a policy (the agent’s strategy for choosing actions) and a value function (which estimates long-term success). Some setups also include a model of the environment, though many RL algorithms operate without one (model-free methods).
The agent-environment interaction forms the foundation of RL. For example, consider a robot learning to navigate a maze. The agent (robot) observes its current state (position in the maze) and selects an action (move left, right, etc.). The environment (maze) updates the robot’s state based on the action and provides a reward (e.g., +100 for reaching the exit, -1 for hitting a wall). The policy might start as random movements but improves over time by maximizing cumulative rewards. Developers often formalize this interaction as a Markov Decision Process (MDP), which assumes the current state contains all information needed to decide the next action. Real-world applications, like training a self-driving car to avoid collisions, follow a similar loop: the car’s sensors provide state data, actions are steering or braking decisions, and rewards reflect safe or unsafe outcomes.
The reward function and value function are critical for balancing immediate and long-term goals. The reward function defines the problem’s objective—for instance, a game-playing AI might receive +1 for winning, -1 for losing, and 0 otherwise. However, rewards alone don’t account for delayed consequences. The value function addresses this by estimating the total rewards an agent can expect from a state onward, discounted by a factor (e.g., 0.9 per step) to prioritize near-term rewards. For example, a delivery drone might value reaching a destination quickly (high immediate reward) but also avoid paths that drain its battery (preventing future penalties). Developers implement algorithms like Q-learning or policy gradients to optimize these functions, often using exploration strategies (e.g., epsilon-greedy) to balance trying new actions versus exploiting known good ones. Understanding these components helps in designing RL systems that learn efficiently and avoid pitfalls like local optima or reward misalignment.