episode - chunhualiao/public-docs GitHub Wiki

In the context of reinforcement learning, an "episode" refers to a single complete sequence of interactions between an agent and its environment. It starts from an initial state and continues until a terminal state is reached (e.g., the task is completed or failed) or a predefined maximum number of steps is taken.

For the train_code_buddy.py script, each episode represents one attempt by the Code Buddy agent to solve a given problem or puzzle. During an episode, the agent takes actions, receives observations and rewards, and updates its internal state. Once an episode concludes, the environment is typically reset, and a new episode begins. The training process runs for a specified number of episodes (in this case, 500), with the agent learning and improving its policy over each episode.