On shallow planning under partial observability

Randy Lefebvre; Audrey Durand

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arXiv · 2407.15820

On shallow planning under partial observability

Randy Lefebvre·Audrey Durand

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Abstract

Formulating a real-world problem under the Reinforcement Learning framework involves non-trivial design choices, such as selecting a discount factor for the learning objective (discounted cumulative rewards), which articulates the planning horizon of the agent. This work investigates the impact of the discount factor on the bias-variance trade-off given structural parameters of the underlying Markov Decision Process. Our results support the idea that a shorter planning horizon might be beneficial, especially under partial observability.

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Randy Lefebvre, Audrey Durand. 2024-07-22. On shallow planning under partial observability. https://arxiv.org/abs/2407.15820

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