Reinforcement learning agents providing advice in complex video games
Autor: | Lisa Torrey, Matthew D. Taylor, Nicholas Carboni, Anestis Fachantidis, Ioannis Vlahavas |
---|---|
Rok vydání: | 2014 |
Předmět: | |
Zdroj: | Connection Science. 26:45-63 |
ISSN: | 1360-0494 0954-0091 |
DOI: | 10.1080/09540091.2014.885279 |
Popis: | This article introduces a teacher–student framework for reinforcement learning, synthesising and extending material that appeared in conference proceedings [Torrey, L., & Taylor, M. E. (2013)]. Teaching on a budget: Agents advising agents in reinforcement learning. {Proceedings of the international conference on autonomous agents and multiagent systems}] and in a non-archival workshop paper [Carboni, N., &Taylor, M. E. (2013, May)]. Preliminary results for 1 vs. 1 tactics in StarCraft. {Proceedings of the adaptive and learning agents workshop (at AAMAS-13)}]. In this framework, a teacher agent instructs a student agent by suggesting actions the student should take as it learns. However, the teacher may only give such advice a limited number of times. We present several novel algorithms that teachers can use to budget their advice effectively, and we evaluate them in two complex video games: StarCraft and Pac-Man. Our results show that the same amount of advice, given at different moments, can have differe... |
Databáze: | OpenAIRE |
Externí odkaz: | |
Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |