PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making
This work addresses the challenge of building intelligent autonomous agents that can handle uncertainties and changes, though it appears incremental as it combines existing methods.
The authors tackled the problem of robust decision-making in dynamic environments by integrating symbolic planning with hierarchical reinforcement learning, resulting in rapid policy search and robust symbolic plans in complex domains.
Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may not be robust to domain uncertainties and changes. In this paper we present a unified framework {\em PEORL} that integrates symbolic planning with hierarchical reinforcement learning (HRL) to cope with decision-making in a dynamic environment with uncertainties. Symbolic plans are used to guide the agent's task execution and learning, and the learned experience is fed back to symbolic knowledge to improve planning. This method leads to rapid policy search and robust symbolic plans in complex domains. The framework is tested on benchmark domains of HRL.