A Survey of Knowledge-based Sequential Decision Making under Uncertainty

Zhang, Shiqi; Sridharan, Mohan

doi:10.48550/arxiv.2008.08548

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…This work aims to enable a robot to represent and reason with contextual information to guide robot planning under uncertainty. Researchers have developed algorithms that reason with contextual knowledge to guide sequential decision making [11]. The contextual knowledge can be in a variety of forms, such as commonsense knowledge [12], action knowledge [13], [14], and graph-based knowledge [7], [15].…”

Section: Related Workmentioning

confidence: 99%

Reasoning with Scene Graphs for Robot Planning under Partial Observability

Amiri¹,

Chandan²,

Zhang³

2022

Preprint

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Robot planning in partially observable domains is difficult, because a robot needs to estimate the current state and plan actions at the same time. When the domain includes many objects, reasoning about the objects and their relationships makes robot planning even more difficult. In this paper, we develop an algorithm called scene analysis for robot planning (SARP) that enables robots to reason with visual contextual information toward achieving long-term goals under uncertainty. SARP constructs scene graphs, a factored representation of objects and their relations, using images captured from different positions, and reasons with them to enable context-aware robot planning under partial observability. Experiments have been conducted using multiple 3D environments in simulation, and a dataset collected by a real robot. In comparison to standard robot planning and scene analysis methods, in a target search domain, SARP improves both efficiency and accuracy in task completion. Supplementary material can be found at https://tinyurl.com/sarp22

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Section: Related Workmentioning

confidence: 99%

Reasoning with Scene Graphs for Robot Planning under Partial Observability

Amiri¹,

Chandan²,

Zhang³

2022

Preprint

Self Cite

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“…In a different direction, exploiting declarative knowledge to construct actions sequences can also help reward shaping to find the optimal policy, as a few studies [118,119] have demonstrated. We refer to the survey by [122] for further examples of studies both in probabilistic planning and RL aiming to reason with declarative domain knowledge.…”

Section: Hierarchical Approachesmentioning

confidence: 99%

A Survey on Interpretable Reinforcement Learning

Glanois¹,

Weng²,

Zimmer³

et al. 2021

Preprint

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Although deep reinforcement learning has become a promising machine learning approach for sequential decision-making problems, it is still not mature enough for high-stake domains such as autonomous driving or medical applications. In such contexts, a learned policy needs for instance to be interpretable, so that it can be inspected before any deployment (e.g., for safety and verifiability reasons). This survey provides an overview of various approaches to achieve higher interpretability in reinforcement learning (RL). To that aim, we distinguish interpretability (as a property of a model) and explainability (as a post-hoc operation, with the intervention of a proxy) and discuss them in the context of RL with an emphasis on the former notion. In particular, we argue that interpretable RL may embrace different facets: interpretable inputs, interpretable (transition/reward) models, and interpretable decision-making. Based on this scheme, we summarize and analyze recent work related to interpretable RL with an emphasis on papers published in the past 10 years. We also discuss briefly some related research areas and point to some potential promising research directions.

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A Survey of Knowledge-based Sequential Decision Making under Uncertainty

Cited by 2 publications

References 16 publications

Reasoning with Scene Graphs for Robot Planning under Partial Observability

Reasoning with Scene Graphs for Robot Planning under Partial Observability

A Survey on Interpretable Reinforcement Learning

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