Continuing tasks in reinforcement learning (RL) are problems where the agent interacts with the environment indefinitely, without a predefined endpoint. Unlike episodic tasks, which have clear start and end points (e.g., winning a game or completing a level), continuing tasks require the agent to learn and act continuously over an infinite time horizon. The goal is to maximize the cumulative reward over the long term, often using a discount factor to prioritize immediate rewards over distant ones. These tasks are common in real-world scenarios where systems operate continuously, such as robotics, resource management, or autonomous systems.
A key challenge in continuing tasks is ensuring the agent learns efficiently without resetting the environment. For example, a robot maintaining balance or a server optimizing energy usage must adapt to dynamic conditions without episodes to “restart.” This requires algorithms that handle non-stationarity—where the environment or reward structure changes over time—and balance exploration (trying new actions) with exploitation (using known effective actions). Techniques like experience replay or adaptive exploration strategies (e.g., decaying epsilon-greedy) are often used to address these challenges. Additionally, since the task never ends, the agent must manage infinite state and action spaces, often requiring function approximation (e.g., neural networks) to generalize learning.
Practical examples include algorithmic trading systems that continuously adjust strategies based on market data or HVAC systems optimizing energy use in real time. In these cases, the agent’s policy is updated incrementally as new data arrives, and the lack of episodic boundaries means traditional evaluation metrics (like episodic rewards) are less meaningful. Instead, metrics like average reward per step or convergence to a stable policy are prioritized. Algorithms like Q-learning with function approximation, actor-critic methods, or policy gradient approaches (e.g., PPO) are commonly applied here. These methods focus on steady, incremental improvement rather than episodic performance, aligning with the indefinite nature of continuing tasks.