Boundary between Agent and Environment

In reinforcement learning, the boundary between the agent and the environment is not necessarily aligned with the physical boundary of a robot or animal’s body. Typically, this boundary is said to be defined closer to the agent.

For example, components like motors, mechanical linkages, and sensing hardware of a robot are generally considered as a part of the environment rather than part of the agent. Similarly, if we think of a person or animal, the muscles, skeleton, and even sensory organs are considered as a part of the environment.

However, the physical computation of rewards falls inside the system (e.g., within a robot), and the agent in reinforcement learning is treated as if they had gained the rewards from the environment.

The general rule in reinforcement learning that cannot be changed arbitrarily is the role of the agent and environment. The agent is capable of making decisions and taking actions based on the current state and the information it holds. It can typically change its actions and strategy based on the learning from its experiences. In contrast, the environment is responsible for producing the necessary information to the agents about its current state.

The environment does not necessarily restrict the essential information from the agents. Instead, it will produce the essential information for the agents to help them decide on the next action. Even if the environment hides the information from the agents because of Markov’s property, the agents might still know how its rewards are computed as a function and what states they have taken in the past.

In general, the agent-environment boundary can be determined only if one has chosen the particular states, actions, and rewards, and also discovered a particular decision-making task of interest.

The agent-environment interface is a fundamental concept of reinforcement learning. It encapsulates the continuous interaction between an autonomous agent and its surrounding environment that forms the basis of how the agents learn from and adapt to their experiences to achieve specific goals. This article explores the decision-making process of agents, the flexibility of the framework, and the critical distinction between the agent and its environment.