In reinforcement learning (RL), the environment refers to everything that an agent interacts with while it learns to make decisions. It's the external system that provides the agent with observations and feedback based on its actions. The environment encompasses not only the rules and dynamics that govern what happens when the agent performs certain actions but also the state information the agent receives to make informed decisions. This could include anything from a game board in a video game to a complex robotic system in a physical space.
To illustrate this, consider a simple RL scenario where an agent is learning to navigate a maze. In this case, the maze itself represents the environment. The walls and pathways define the rules of the environment, and the agent can take actions such as moving left, right, up, or down. After taking an action, the agent receives feedback in the form of a new state (its new position in the maze) and a reward (e.g., a positive score for moving closer to the exit or a negative score for hitting a wall). These interactions help the agent learn the best strategies for reaching the goal effectively.
The environment can be either deterministic or stochastic. In deterministic environments, the outcome of each action is predictable; for example, moving one step north always leads to the same new position. In contrast, stochastic environments involve randomness; for instance, the outcome of an action could depend on external factors like weather conditions or user actions in a game. Developers designing RL systems need to model their environments accurately to ensure that the agent learns effectively, which includes outlining the states, possible actions, and the reward system.