What are episodic tasks in reinforcement learning?

Episodic tasks in reinforcement learning are scenarios where an agent’s interaction with the environment is divided into distinct, self-contained episodes. Each episode has a clear starting point and a terminal state that marks its end. The agent’s goal is to maximize the cumulative reward within an episode, and after termination, the environment resets to its initial state. This structure allows the agent to repeatedly practice and learn from independent trials without the need to handle infinite interactions. For example, a game like chess can be an episodic task because each match starts with the board in a standard configuration and ends when checkmate or a draw occurs.

A key characteristic of episodic tasks is that the agent’s performance can be evaluated by averaging results across multiple episodes. This makes it easier to measure progress, as each episode provides a complete trajectory of states, actions, and rewards. Common examples include video games (e.g., Super Mario, where a level ends when the player wins or loses), robotic simulations (e.g., a robot arm tasked with picking up an object within a time limit), or training a self-driving car in a simulated environment where episodes reset after a collision or successful navigation. These bounded episodes simplify experimentation because developers can test algorithms on finite sequences of interactions and compare outcomes systematically.

Episodic tasks influence how reinforcement learning algorithms are designed. For instance, Monte Carlo methods rely on complete episode trajectories to estimate value functions, as they require knowledge of the total reward from start to termination. In contrast, temporal difference (TD) learning can update estimates incrementally within an episode. Episodic frameworks also enable techniques like experience replay, where past episodes are stored and reused for training, improving sample efficiency. However, developers must handle terminal states carefully—for example, ensuring the agent stops taking actions once an episode ends. This structure is particularly useful for benchmarking, as it allows clear performance comparisons across algorithms by measuring metrics like average reward per episode or success rates over multiple trials.