Hierarchical Reinforcement Learning with Timed Subgoals
This addresses the limitation of subgoal-based HRL methods in dynamic environments, which is incremental as it builds on existing HRL approaches.
The paper tackles the problem of hierarchical reinforcement learning (HRL) struggling with dynamic environments by introducing HiTS, which uses timed subgoals to specify both goal states and timing, enabling sample-efficient learning on challenging dynamic tasks where a state-of-the-art method fails.
Hierarchical reinforcement learning (HRL) holds great potential for sample-efficient learning on challenging long-horizon tasks. In particular, letting a higher level assign subgoals to a lower level has been shown to enable fast learning on difficult problems. However, such subgoal-based methods have been designed with static reinforcement learning environments in mind and consequently struggle with dynamic elements beyond the immediate control of the agent even though they are ubiquitous in real-world problems. In this paper, we introduce Hierarchical reinforcement learning with Timed Subgoals (HiTS), an HRL algorithm that enables the agent to adapt its timing to a dynamic environment by not only specifying what goal state is to be reached but also when. We discuss how communicating with a lower level in terms of such timed subgoals results in a more stable learning problem for the higher level. Our experiments on a range of standard benchmarks and three new challenging dynamic reinforcement learning environments show that our method is capable of sample-efficient learning where an existing state-of-the-art subgoal-based HRL method fails to learn stable solutions.