Adaptive Online Planning for Continual Lifelong Learning
This addresses the challenge of catastrophic failures and performance degradation in lifelong learning for control systems, though it appears incremental as it builds on existing model-based and model-free methods.
The paper tackles the problem of online reset-free lifelong learning in control tasks, where mistakes can compound and dynamics may change, by introducing Adaptive Online Planning (AOP), which combines model-based planning with model-free learning to achieve strong performance and reduce computation times.
We study learning control in an online reset-free lifelong learning scenario, where mistakes can compound catastrophically into the future and the underlying dynamics of the environment may change. Traditional model-free policy learning methods have achieved successes in difficult tasks due to their broad flexibility, but struggle in this setting, as they can activate failure modes early in their lifetimes which are difficult to recover from and face performance degradation as dynamics change. On the other hand, model-based planning methods learn and adapt quickly, but require prohibitive levels of computational resources. We present a new algorithm, Adaptive Online Planning (AOP), that achieves strong performance in this setting by combining model-based planning with model-free learning. By approximating the uncertainty of the model-free components and the planner performance, AOP is able to call upon more extensive planning only when necessary, leading to reduced computation times, while still gracefully adapting behaviors in the face of unpredictable changes in the world -- even when traditional RL fails.