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A state is a configuration of an agent and its environment and represents the configuration or situation of the agent and the environment at a particular point in time. It essentially encapsulates all the relevant information that the agent needs to make decisions. It is a snapshot that captures the current conditions of the system, providing context for the agent's actions. The state is often used as input for the agent's decision-making process, and how the state changes influences the agent's future actions. In reinforcement learning, the sequence of states, actions, and rewards is crucial for the learning process.

State space is defined as the set of all possible states that can be reached from the initial state by applying a sequence of actions. The state space represents the entire range of possible situations or configurations that the system can be in during its operation.

State space exploration applies sequences of actions in the system, explores and reaches different states in the state space. 

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