Chapter 10. Explainable Neuro-Symbolic Hierarchical Reinforcement Learning

Human-robot interactive decision-making is increasingly becoming ubiquitous, and explainability is an influential factor in determining the reliance on autonomy. However, it is not reasonable to trust systems beyond our comprehension, and typical machine learning and data-driven decision-making are black-box paradigms that impede explainability. Therefore, it is critical to establish computational efficient decision-making mechanisms enhanced by explainability-aware strategies. To this end, we propose the Trustworthy Decision-Making (TDM), which is an explainable neuro-symbolic approach by integrating symbolic planning into hierarchical reinforcement learning. The framework of TDM enables the subtask-level explainability from the causal relational and understandable subtasks. Besides, TDM also demonstrates the advantage of the integration between symbolic planning and reinforcement learning, reaping the benefits of both worlds. Experimental results validate the effectiveness of proposed method while improving the explainability in the process of decision-making.

Logic-Based Sequential Decision-Making

Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better. In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planning. The task-level interpretability is enabled by relating symbolic actions to options. This framework features a planner – controller – meta-controller architecture, which takes charge of subtask scheduling, data-driven subtask learning, and subtask evaluation, respectively. The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input. Experimental results validate the interpretability of subtasks, along with improved data efficiency compared with state-of-the-art approaches.

SDRL: Interpretable and Data-Efficient Deep Reinforcement Learning Leveraging Symbolic Planning

Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better. In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planning. The task-level interpretability is enabled by relating symbolic actions to options.This framework features a planner – controller – meta-controller architecture, which takes charge of subtask scheduling, data-driven subtask learning, and subtask evaluation, respectively. The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input. Experimental results validate the interpretability of subtasks, along with improved data efficiency compared with state-of-the-art approaches.

PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making

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 PEORL that integrates symbolic planning with hierarchical reinforcement learning (HRL) to cope with decision-making in 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.