Agent-environment Interface in AI

Artificial intelligence‘s reinforcement learning field focuses on how agents should behave in environments to maximize cumulative rewards. To accomplish a certain objective, the agent must learn from interactions with the environment in the reinforcement learning problem.

• Agent: The agent is said to be the learner or a decision-maker who is capable of interacting with their surroundings to achieve a specific goal.
• Environment: Everything external to the agent that the agent interacts with is called the environment. This includes all the conditions, contexts, and dynamics that the agent must respond to.

The presence of continuous interaction allows the agent to select the actions while the environment is responsible for responding to those actions thereby presenting a new situation to the agent. The environment grants the rewards typically in numerical values where the agent seeks to maximize it over time. The complete specification of an environment includes all the necessary details for the agent to understand and interact with it. This specification defines the tasks, which is a particular instance of the reinforcement learning problem.

Time steps and continual interaction

The agent-environment interaction in reinforcement learning is structured as a sequence of discrete time steps.

• Discrete-time steps: The interaction occurs at each of a sequence of discrete time steps that is denoted as t. (Where t = 0,1,2,3,… ). At each time step t, a series of events unfolds so that it can dictate the agent’s learning and decision-making process.
• Continual interaction: The continual interaction refers to a scenario in reinforcement learning where the interaction between the agent and the environment does not naturally break down into distinct or separate episodes. Instead, the interaction continues indefinitely without a predefined endpoint.

Perception, Action, and Feedback

• Perception: Perception refers to the process by which the agent gathers all the information about the environment. At each time step t, the agent perceives the current state information of the environment that is denoted as S_t. This state helps to produce all the relevant information to the agent so that it can make a decision based on it. The perception process involves sensing and interpreting data that can be received from various sensors or inputs depending upon the specific tasks.
• Action: Action is the process by which the agent responds to the perceived state of the environment. Based on the state S_t, the agent is capable of choosing an appropriate action At from the set of possible actions A(S_t).
• Feedback: Feedback is the information that the agent receives from the environment after acting. The feedback can be of two forms:
  • New states(S_t+1): After taking an action A_t, the environment transitions to a new state S_t+1. This new state reflects the updated situation in the environment as a result of the agent’s actions.
  • Reward(R_t+1): The agent receives the numerical reward R_t+1, which provides the measure of immediate effect of the action. The reward earned by the agent can be beneficial or it may negative impact. The agent uses these rewards to adjust its policy to maximize the cumulative rewards over time.

Example: Consider a robot in a maze-navigation task. The agent robot aims to find the maze’s exit to earn a positive reward. The perception, action, and feedback for this scenario can be described as follows,

Representation of State, Action and Rewards

• State representation: At each time step t, the agent receives the representation of the environment’s state, thus, this can be denoted as [Tex]S_{t} \epsilon S[/Tex], where the S represents the set of possible states.
• Action selection: Based on the state S_t, the agent now selects an action A_t. The A_t is chosen from the A(S_t), which represents the set of all actions available to the agent when it is in the state S_t.
• Reward and new state: One-time step later, after the agent has taken the action A_t, it now receives a numerical reward R_t+1. This reward indicates the feedback signal for the immediate impact of the action taken by the agent, and it belongs to R that is a subset of real numbers. Meanwhile, the agent finds itself in a new state S_t+1, this new state is obtained as a result of previous action A_t taken by the agent.

Example Scenario: Consider the scenario where the robot’s goal is to learn to walk. The researchers have assigned rewards on each time step proportional to the robot’s forward motion. The robot might receive positive reward for moving forward and negative rewards for bumping into obstacles. The robot’s actions determine whether it receives positive or negative rewards.

The state, action, and reward in this scenario are as follows:

• State(S_t): The robot’s current physical configuration and position
• Action(A_t): Movements or adjustments in the robot’s joints and limbs.
• Reward(R_t+1): Proportional to the robot’s forward motion, encouraging walking. Negative rewards may be given for undesirable actions such as bumping into obstacles.

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.