In the updated Gymnasium environment interface, the distinction between “terminated” and “truncated” provides more clarity on why an episode ended, which is useful for more nuanced reinforcement learning algorithms and analysis.
Terminated:
An episode is “terminated” when it comes to a natural end due to the environment’s conditions. This means the agent has reached a terminal state due to the inherent dynamics of the environment. Examples include:
- The agent has achieved the goal (e.g., reaching the end of a maze).
- The agent has failed catastrophically (e.g., losing all lives in a game).
- A condition that defines a natural conclusion of the episode is met (e.g., a robot successfully completing or failing a task).
Termination indicates that the episode has concluded in a way that is consistent with the environment’s rules or objectives.
Truncated:
An episode is “truncated” when it ends due to external conditions not related to the primary objectives or rules of the environment. This is often due to:
- A maximum step limit being reached, which is common in training to prevent agents from getting stuck in long or infinite loops.
- An intervention by an external process, perhaps for safety reasons in real-world applications or due to resource limitations in simulation.
Truncation is a way to forcibly end an episode for reasons that are external to the environment’s natural conclusion.
Implications for Reinforcement Learning:
The distinction between “terminated” and “truncated” is important for training and evaluating reinforcement learning algorithms. It can influence how an algorithm treats the final state of an episode or how it adjusts its learning process. For example:
- If an episode was “terminated,” the algorithm might learn that the final state is a natural outcome of its actions, which could be either positive (achieving a goal) or negative (catastrophic failure).
- If an episode was “truncated,” the algorithm might treat the final state differently, knowing that it was an artificial end to the episode not caused by the agent’s actions or the environment’s natural dynamics.
This distinction helps in more accurately evaluating an agent’s performance and in designing learning algorithms that can distinguish between different types of episode endings.