Exploration and value function factorisation in single and multi-agent reinforcement learning

Exploration and value function factorisation in single and multi-agent reinforcement learning

<p>The ability to learn from data is crucial in developing satisfactory solutions to many complex problems. In particular, in the design of intelligent agents that exist and interact with a complex environment in the pursuit of some goal. In this thesis we investigate some bottlenecks that can...

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Dettagli Bibliografici
Autore principale:	Rashid, T
Altri autori:	Whiteson, S
Natura:	Tesi
Lingua:	English
Pubblicazione:	2021
Soggetti:	Reinforcement learning

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