On shallow planning under partial observability
<|reference_start|>On shallow planning under partial observability: 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 biasvariance 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.<|reference_end|>
@article{lefebvre2024on,
    title={On shallow planning under partial observability},
    author={Randy Lefebvre, Audrey Durand},
    journal={arXiv preprint arXiv:2407.15820},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15820},
    primaryClass={cs.AI}
}