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Adaptive Policy Gradient in Multiagent Learning
Bikramjit Banerjee,
Jing Peng
Computer Science
Research output
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Contribution to conference
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Paper
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peer-review
44
Scopus citations
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Dive into the research topics of 'Adaptive Policy Gradient in Multiagent Learning'. Together they form a unique fingerprint.
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Keyphrases
Self-play
100%
Multi-agent Learning
100%
Action Games
100%
Adaptive Policy
100%
Policy Gradient
100%
General-sum Games
50%
Equilibrium Policies
50%
Convergence of Nash Equilibria
50%
Fast Convergence
50%
Policy Gradient Learning
50%
Game Scenarios
50%
Computable
50%
Learning Rate
50%
Hill Climbing
50%
Rate Ratio
50%
Computer Science
Approximation (Algorithm)
100%
Nash Equilibrium
100%
multiagent learning
100%
Fast Convergence
100%
Learning Rate
100%
Alternative Version
100%
Hill Climbing
100%
Economics, Econometrics and Finance
Nash Equilibrium
100%