In the field of reinforcement learning (RL), tasks are generally categorized into two main types based on their structural properties: episodic tasks and continuous tasks. Understanding the distinction between these types is crucial for the design and implementation of RL algorithms, as each type presents unique challenges and requirements.
Episodic tasks are scenarios where the learning process is divided into separate episodes. Each episode represents a complete sequence of interactions between the agent and the environment, starting from an initial state and ending in a terminal state. The terminal state can be reached either through the agent’s actions or by satisfying a predefined condition, such as reaching a goal or a time limit. The agent’s objective in episodic tasks is to maximize the cumulative reward over each episode, which allows for clear assessment and adjustment after each complete cycle. Common examples include games like chess or board games, where each match has a clear beginning and end.
In contrast, continuous tasks involve scenarios where there is no natural endpoint, and the agent interacts with the environment in an ongoing, indefinite manner. Here, the agent’s goal is to maximize the average reward per time step over an infinite horizon. Continuous tasks are more representative of real-world problems, such as robotic control or stock trading, where the agent must make decisions continuously without a reset to an initial state. These tasks require strategies that can adapt to changing conditions over time, without relying on episodic feedback.
The choice between episodic and continuous task structures impacts the design of the RL algorithm and its evaluation metrics. Episodic tasks often utilize metrics like episode length and total reward per episode, which provide straightforward insights into the agent’s performance. In continuous tasks, performance is typically evaluated based on metrics such as average reward rates or discounted cumulative rewards, reflecting the ongoing nature of the task.
Implementing RL solutions necessitates careful consideration of these task structures, as each requires different approaches to state representation, reward shaping, and exploration-exploitation strategies. In episodic tasks, exploration might be reset after each episode, allowing the agent to refine its strategy with each new attempt. Continuous tasks, however, demand strategies that balance exploration and exploitation over time to ensure sustained performance improvements.
By understanding the distinctions and requirements of episodic and continuous tasks, practitioners can better tailor their RL models to meet the specific demands of the environment they are working with, ultimately leading to more effective learning and decision-making processes.