Open AccessProceedings Article
Rational and convergent learning in stochastic games
Michael Bowling,Manuela Veloso +1 more
- pp 1021-1026
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This paper introduces two properties as desirable for a learning agent when in the presence of other learning agents, namely rationality and convergence, and contributes a new learning algorithm, WoLF policy hillclimbing, that is proven to be rational.Abstract:
This paper investigates the problem of policy learning in multiagent environments using the stochastic game framework, which we briefly overview. We introduce two properties as desirable for a learning agent when in the presence of other learning agents, namely rationality and convergence. We examine existing reinforcement learning algorithms according to these two properties and notice that they fail to simultaneously meet both criteria. We then contribute a new learning algorithm, WoLF policy hillclimbing, that is based on a simple principle: “learn quickly while losing, slowly while winning.” The algorithm is proven to be rational and we present empirical results for a number of stochastic games showing the algorithm converges.read more
Citations
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References
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Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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Decisions with Multiple Objectives: Preferences and Value Trade-Offs
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Equilibrium points in n-person games
TL;DR: A concept of an n -person game in which each player has a finite set of pure strategies and in which a definite set of payments to the n players corresponds to each n -tuple ofpure strategies, one strategy being taken for each player.
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Introduction to Reinforcement Learning
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.