The environment in reinforcement learning (RL) represents everything outside the agent that the agent interacts with. It includes all factors that affect the agent’s state and the feedback it receives. The environment responds to the actions taken by the agent by transitioning to a new state and providing rewards or penalties, which the agent uses to adjust its behavior.
The environment is essential for the learning process, as it serves as the source of feedback that guides the agent’s decisions. It is typically modeled as a system that evolves in response to the agent's actions, providing different states, rewards, or penalties based on the agent’s behavior. For example, in a game, the environment could represent the game world, and the agent could be a character or object within it.
The environment’s dynamics define how challenging the learning process is for the agent. In more complex environments, the agent needs to explore and adapt its behavior to deal with uncertainty, multiple possible actions, and delayed rewards. The interaction between the agent and the environment is key to reinforcement learning.