In reinforcement learning (RL), an environment is the external system with which the agent interacts and from which the agent receives feedback in the form of rewards or penalties. The environment encapsulates the dynamics of the problem the agent is trying to solve and determines the consequences of the agent's actions.
Key characteristics of the environment in RL include :
* States : The environment has a set of possible states, representing the different configurations or situations it can be in. At each time step, the environment is in a particular state, and the agent's actions influence transitions between states.
* Actions : The environment defines a set of possible actions that the agent can take. These actions represent the decisions or moves available to the agent at each state. The agent's goal is to learn which actions to take in different states to maximize its cumulative rewards.
* Transitions : When the agent takes an action in a particular state, the environment transitions to a new state according to its dynamics. The transition function specifies the probabilities of transitioning to each possible next state given the current state and action.
* Rewards : After each action taken by the agent, the environment provides feedback in the form of a reward signal. The reward signal indicates the immediate desirability or undesirability of the action taken by the agent in the current state. The agent's objective is typically to maximize the cumulative reward over time.
* Termination : In some cases, the environment has terminal states where the episode ends. Terminal states are reached when certain conditions are met, such as achieving a goal or encountering a failure condition. The termination of an episode signals the end of a sequence of interactions between the agent and the environment.
