Average Reward Q-learning
Average Reward Q-Learning is a reinforcement learning algorithm that extends the standard Q-Learning framework by optimizing the expected average reward per time step rather than the discounted cumulative reward, thereby focusing on long-term steady-state performance in ergodic Markov decision processes.
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2017–2026
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Lenient Multi-Agent Deep Reinforcement Learning
Gregory Palmer, Karl Tuyls, Daan Bloembergen et al. · 2018 · 83 citations · Full text
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Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
4 h-index
4 publications
245 citations

Indian Institute of Science Bangalore
Bengaluru, India
3 h-index
4 publications
202 citations
California Southern University
Chandler, United States
2 h-index
5 publications
139 citations

University of Alberta
Edmonton, Canada
2 h-index
4 publications
242 citations
University of Southern California
Los Angeles, United States
2 h-index
5 publications
139 citations
National Electronics and Computer Technology Center
Pathum Thani, Thailand
1 h-index
1 publications
19 citations
AT&T (United States)
Bedminster, United States
1 h-index
1 publications
18 citations
University at Buffalo, State University of New York
Buffalo, United States
1 h-index
1 publications
96 citations
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Repository · 4 publications · 63 citations
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Conference · 2 publications · 50 citations
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Repository · 1 publications · 12 citations
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1 publications · 22 citations
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