En apprentissage par renforcement, l’environnement est le monde, la simulation, le système ou le contexte de tâche avec lequel un agent interagit. Il fournit des observations ou des états, reçoit les actions de l’agent et passe à de nouveaux états selon sa dynamique.
In reinforcement learning, the world that contains the agent and allows the agent to observe that world's state. For example, the represented world can be a game like chess, or a physical world like a maze. When the agent applies an action to the environment, then the environment transitions between states.
In reinforcement learning, the world that contains the agent and allows the agent to observe that world's state. For example, the represented world can be a game like chess, or a physical world like a maze. When the agent applies an action to the environment, then the environment transitions between states.