Reinforcement Learning

 

After several "AI winters" where there was certain pessimism about our ability to improve the performance of intelligent systems. Today, AI is flourishing thanks to machine learning (ML) advances. Distributed reinforcement learning is one of the essential technologies that can support ML innovation. This book discusses distributed aspects of AI agents where multiple systems seek to maximize their reward functions and contribute to making decisions. The authors assembled an impressive collection of materials that take the reader through analyzing reward distribution in the context of incremental and temporal-difference learning.

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