Convergent multiple-timescales reinforcement learning algorithms in normal form games

DOI10.1214/aoap/1069786497zbMath1084.68102OpenAlexW2067018002MaRDI QIDQ1429103

E. J. Collins, David S. Leslie

Publication date: 30 March 2004

Published in: The Annals of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoap/1069786497

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) (n)-person games, (n>2) (91A06) Stochastic approximation (62L20) Multistage and repeated games (91A20)

Related Items (8)

A note on adjusted replicator dynamics in iterated games ⋮ Asynchronous stochastic approximation with differential inclusions ⋮ Online calibrated forecasts: memory efficiency versus universality for learning in games ⋮ Continuous-Time Convergence Rates in Potential and Monotone Games ⋮ Towards multi‐agent reinforcement learning‐driven over‐the‐counter market simulations ⋮ Weak convergence of dynamical systems in two timescales ⋮ Emergence of information transfer by inductive learning ⋮ Generalised weakened fictitious play

Cites Work

• Unnamed Item
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• Nonconvergence to unstable points in urn models and stochastic approximations
• Stochastic approximation methods for constrained and unconstrained systems
• Learning mixed equilibria
• Three problems in learning mixed-strategy Nash equilibria
• Stochastic approximation with two time scales
• Mixed equilibria and dynamical systems arising from fictitious play in perturbed games
• A note on best response dynamics.
• Learning in perturbed asymmetric games
• Games with randomly disturbed payoffs: a new rationale for mixed-strategy equilibrium points
• Non-cooperative games
• The allocation of offensive and defensive resources in a territorial game
• REINFORCEMENT LEARNING IN MARKOVIAN EVOLUTIONARY GAMES
• Actor-Critic--Type Learning Algorithms for Markov Decision Processes

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