VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts
<|reference_start|>VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts: Despite the considerable attention given to the questions of \textit{how much} and \textit{how to} explore in deep reinforcement learning, the investigation into \textit{when} to explore remains relatively less researched. While more sophisticated exploration strategies can excel in specific, often sparse reward environments, existing simpler approaches, such as $\epsilon$-greedy, persist in outperforming them across a broader spectrum of domains. The appeal of these simpler strategies lies in their ease of implementation and generality across a wide range of domains. The downside is that these methods are essentially a blind switching mechanism, which completely disregards the agent's internal state. In this paper, we propose to leverage the agent's internal state to decide \textit{when} to explore, addressing the shortcomings of blind switching mechanisms. We present Value Discrepancy and State Counts through homeostasis (VDSC), a novel approach for efficient exploration timing. Experimental results on the Atari suite demonstrate the superiority of our strategy over traditional methods such as $\epsilon$-greedy and Boltzmann, as well as more sophisticated techniques like Noisy Nets.<|reference_end|>
@article{captari2024vdsc:,
    title={VDSC: Enhancing Exploration Timing with Value Discrepancy and State
  Counts},
    author={Marius Captari, Remo Sasso, Matthia Sabatelli},
    journal={arXiv preprint arXiv:2403.17542},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17542},
    primaryClass={cs.LG cs.AI}
}