VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910384102834176 |
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| author | Captari, Marius Sasso, Remo Sabatelli, Matthia |
| author_facet | Captari, Marius Sasso, Remo Sabatelli, Matthia |
| contents | 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 $ε$-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 $ε$-greedy and Boltzmann, as well as more sophisticated techniques like Noisy Nets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17542 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts Captari, Marius Sasso, Remo Sabatelli, Matthia Machine Learning Artificial Intelligence 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 $ε$-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 $ε$-greedy and Boltzmann, as well as more sophisticated techniques like Noisy Nets. |
| title | VDSC: Enhancing Exploration Timing with Value Discrepancy and State Counts |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2403.17542 |