https://marcoph.org/authors/yang-li/Yang LiWowchemy (https://wowchemy.com)en-us© 2026 mperhezWed, 01 Jan 2025 00:00:00 +0000https://marcoph.org/media/icon_hu0b7a4cb9992c9ac0e91bd28ffd38dd00_9727_512x512_fill_lanczos_center_2.pnghttps://marcoph.org/authors/yang-li/https://marcoph.org/publication/scholar-behavioural-analysis-of-independent-value-based-learning-in-non-cooperative-games-giygafsaaaaj-7pzlfssx8tac/Wed, 01 Jan 2025 00:00:00 +0000https://marcoph.org/publication/scholar-behavioural-analysis-of-independent-value-based-learning-in-non-cooperative-games-giygafsaaaaj-7pzlfssx8tac/<h1 id="abstract">Abstract</h1> <p>Multi-agent reinforcement learning has received increased attention in cooperative games. However, research in non-cooperative games is lagging behind. Independent value-based learning algorithms have demonstrated simplicity and versatility in various contexts. In this paper, we study the behavior of these algorithms in non-cooperative settings. We explain the conditions that a game must satisfy for the algorithms to work. We further test the algorithms in our proposed game Food Chain that simulates an ecosystem. Our results show that independent value-based learning algorithms can converge to Nash equilibrium, only when the Nash equilibrium consists of uniformly random policies over the feasible actions.</p> <!-- Auto-generated recovery from existing publication HTML. --> <p>Source: <a href="https://www.tandfonline.com/doi/abs/10.1080/17445760.2025.2601000" target="_blank" rel="noopener">https://www.tandfonline.com/doi/abs/10.1080/17445760.2025.2601000</a></p>