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An "episode" in reinforcement learning refers to the complete process in which an agent starts from the initial state of the environment, goes through a series of actions and state transitions, until a termination condition is reached.
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11/1/2026
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UnverifiedEnvironment interaction in reinforcement learning is a closed-loop process: the agent takes actions in the environment, the environment returns state transitions and reward signals, and the agent updates its policy accordingly.73% similarUnverifiedIn the context of reinforcement learning, a rollout refers to the process of an agent completing one full pass through an environment or policy to generate a single trajectory.69% similarVerifiedThe agent loop follows a 'reasoning-action-observation' paradigm, iterating repeatedly until the goal is achieved or a stopping condition is reached.66% similarUnverifiedThe most critical characteristic of the agent loop is that the number of iterations is unknown in advance, and the action sequence emerges step by step based on task state during execution.66% similarUnverifiedThe real process by which an agent handles one task is a loop: understand the goal → decompose and plan → retrieve context → invoke tools → execute actions → check results → if not passing, retry or escalate to a human; only the first of the seven steps is related to conversation.66% similar
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