A state in reinforcement learning (RL) is a representation of the current situation or configuration of an environment that an agent interacts with. It encapsulates all relevant information the agent needs to make decisions at a given time. For example, in a robot navigation task, the state might include the robot’s current position, the layout of obstacles, and the location of the target. The state serves as the input to the agent’s policy, which determines the next action to take. Without a well-defined state, the agent cannot effectively learn or act, as it lacks context about the environment’s dynamics.
States can vary in complexity depending on the problem. In simple cases, like a grid-world game, the state might be a discrete set of coordinates (e.g., (x, y) positions). In more complex environments, such as a video game, the state could be a high-dimensional array of pixel values from the screen. The choice of state representation is critical: it must balance completeness (including enough detail for decision-making) and simplicity (avoiding unnecessary complexity). For instance, in a self-driving car simulation, the state might include the car’s speed, steering angle, nearby vehicles, and road boundaries, but exclude irrelevant details like weather conditions if they aren’t part of the task. Poorly designed states can lead to inefficient learning or failure to solve the problem.
A key challenge in RL is handling partial observability, where the agent doesn’t have access to the full state. For example, in a poker game, a player can’t see opponents’ cards, so the state is limited to their own hand and visible community cards. This is formalized as a Partially Observable Markov Decision Process (POMDP). In such cases, agents often rely on histories of observations or learned representations to infer the underlying state. Designing effective states—whether raw sensor data, engineered features, or learned embeddings—is foundational to building RL systems that generalize and perform robustly in real-world applications.