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- W4214911441 abstract "This paper proposes a class of stochastic decision problems, which we call Contextual Shortest Path (CSP) problems. The problem is motivated by dynamic path planning and obstacle avoidance for UAV and drone applications. More specifically, in path planning applications, there is a need to compute the path between two pre-determined locations in space while avoiding various spontaneous obstacles in the environment. In general, finding the optimal path in a stochastic environment with time-varying obstacles generalizes the problem of finding the shortest path over an undirected graph with stochastic edges, where random edge realizations are augmented with richer contexts that can change over time and require online inspection.Under perfect knowledge of context distributions, we provide an extended Dijkstra’s algorithm to solve the associated dynamic program efficiently. When the context distributions are unknown and need to be learned online, we first adapt two algorithms as our baselines, one based on Thompson Sampling and the other based on ϵ-greedy exploration. We then propose a novel reinforcement learning algorithm, RL-CSP, which intelligently distributes exploration episodes over the time horizon and ensures the agent visits under-explored states. We bound the regret for RL-CSP, and augment the theoretical results with simulations over various network topologies. We further demonstrate the improved robustness of our RL-based algorithm in the stochastic shortest path setting." @default.
- W4214911441 created "2022-03-05" @default.
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- W4214911441 date "2021-10-31" @default.
- W4214911441 modified "2023-09-29" @default.
- W4214911441 title "Contextual Shortest Path with Unknown Context Distributions" @default.
- W4214911441 doi "https://doi.org/10.1109/ieeeconf53345.2021.9723097" @default.
- W4214911441 hasPublicationYear "2021" @default.
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