In reinforcement learning, an episode is one complete attempt or trajectory in which an agent interacts with an environment. It typically begins at an initial state and ends when a terminal condition, success, failure, or time limit is reached.
In reinforcement learning, each of the repeated attempts by the agent to learn an environment.