What is a state in reinforcement learning?

A state in reinforcement learning (RL) is a representation of the current situation or environment that an agent uses to make decisions. It encapsulates all relevant information the agent needs to determine its next action. For example, in a game of chess, the state could include the positions of all pieces on the board, whose turn it is, and any rules about check or castling. The state is a foundational concept because it defines the “context” in which the agent operates, enabling it to learn which actions lead to rewards or penalties over time.

States are critical because they allow the agent to reason about the environment systematically. In RL, the agent interacts with the environment by observing states, taking actions, and receiving rewards. The state must contain enough information to make optimal decisions without redundancy. For instance, a self-driving car’s state might include speed, sensor data, nearby vehicles, and traffic signals. However, not all states are fully observable. In partially observable environments (like poker, where you can’t see opponents’ cards), the agent might use a history of observations to approximate the true state. This distinction between fully and partially observable states is key to designing RL systems, as it influences whether algorithms like Q-learning (for fully observable cases) or POMDP-based approaches (for partial observability) are appropriate.

Designing states effectively requires balancing completeness and computational efficiency. If a state includes too much information (e.g., raw pixel data from a game), it becomes high-dimensional and harder to process. Techniques like function approximation (using neural networks) or feature engineering (extracting key details like object positions) help manage complexity. For example, in Atari’s Breakout, a state might be represented as a stack of grayscale frames to capture ball movement, rather than raw RGB pixels. Poorly designed states can lead to slow learning or suboptimal policies. Developers often experiment with state representations—such as discretizing continuous values (like temperature ranges) or using embeddings—to improve an agent’s ability to generalize and act efficiently. The choice of state representation directly impacts the feasibility and performance of RL solutions.