Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment

• URL: http://arxiv.org/abs/2312.03263v3
• Date: Thu, 7 Mar 2024 22:42:20 GMT
• Title: Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment
• Authors: Gokul Puthumanaillam, Xiangyu Liu, Negar Mehr and Melkior Ornik
• Abstract summary: Environmental variability over time can significantly impact the system's optimal decision making strategy. We propose a two-pronged approach to accurately estimate and plan within the TV-POMDP. We validate the proposed framework and algorithms using simulations and robots.
• Score: 14.646280719661465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimal decision-making presents a significant challenge for autonomous systems operating in uncertain, stochastic and time-varying environments. Environmental variability over time can significantly impact the system's optimal decision making strategy for mission completion. To model such environments, our work combines the previous notion of Time-Varying Markov Decision Processes (TVMDP) with partial observability and introduces Time-Varying Partially Observable Markov Decision Processes (TV-POMDP). We propose a two-pronged approach to accurately estimate and plan within the TV-POMDP: 1) Memory Prioritized State Estimation (MPSE), which leverages weighted memory to provide more accurate time-varying transition estimates; and 2) an MPSE-integrated planning strategy that optimizes long-term rewards while accounting for temporal constraint. We validate the proposed framework and algorithms using simulations and hardware, with robots exploring a partially observable, time-varying environments. Our results demonstrate superior performance over standard methods, highlighting the framework's effectiveness in stochastic, uncertain, time-varying domains.

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