Xiaofeng Wang, Tuomas Sandholm  Home/Search
Context Related
| books.nips.cc/papers/files...CN08.ps.gz Cached: PS.gz PS PDF This document uses CoBlitz to cache paper downloads. If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
Image Update HelpPS.gz PS PDF From: books.nips.cc/nips15 (more) (Enter author homepages) |
Rate this article:      (best)Comment on this article |
Abstract: Multiagent learning is a key problem in AI. In the presence of multiple Nash equilibria, even agents with non-conflicting interests may not be able to learn an optimal coordination policy. The problem is exaccerbated if the agents do not know the game and independently receive noisy payoffs. So, multiagent reinforfcement learning involves two interrelated problems: identifying the game and learning to play. In this paper, we present optimal adaptive learning, the first algorithm that... (Update)
Similar documents based on text: More All
0.8: Reinforcement Learning to Play an Optimal Nash Equilibrium in .. - Wang, Sandholm (2002) (Correct)
0.3: Learning Near-Pareto-Optimal Conventions in Polynomial Time - Wang, Sandholm (2003) (Correct)
0.3: Learning Near-Pareto-Optimal Conventions in - Polynomial Time Xiaofeng (Correct)
BibTeX entry: (Update)
X. Wang and T. Sandholm. Reinforcement learning to play an optimal nash equilibrium in team markov games. In Advances in Neural Information Processing Systems 15 (NIPS-2002. http://citeseer.ist.psu.edu/article/wang02reinforcement.html More
@misc{ wang02reinforcement,
author = "X. Wang and T. Sandholm",
title = "Reinforcement learning to play an optimal nash equilibrium in team markov
games",
text = "X. Wang and T. Sandholm. Reinforcement learning to play an optimal nash
equilibrium in team markov games. In Advances in Neural Information Processing
Systems 15 (NIPS-2002.",
year = "2002",
url = "citeseer.ist.psu.edu/article/wang02reinforcement.html" }
Citations (may not include all citations):268 Dynamic programming and Markov processes (context) - Howard - 1960
135 Multi-agent reinforcement learning: independent vs - Tan - 1993
126 The theory of learning in games (context) - Fudenberg, Levine - 1998
99 Multiagent reinforcement learning: theoretical framework and.. - Hu, Wellman - 1998
85 The dynamics of reinforcement learning in cooperative multi-.. - Claus, Boutilier - 1998
83 Learning to coordinate without sharing information - Sen, Sekaran et al. - 1994
38 Convergence results for single-step on-policy reinforcement .. - Singh, Jaakkola et al. - 2000
33 learning and coordination in multi-agent decision processes (context) - Boutilier - 1996
33 Learning to coordinate actions in multi-agent systems (context) - Wei - 1993
24 Markov chain: theory and applications (context) - Isaacson, Madsen - 1976
23 and long run equilibria in games (context) - Kandori, Mailath et al. - 1993
21 Friend-or-Foe Q-learning in general sum game - Littman - 2001
17 Spieltheoretische behandlung eines oligopolmodells mit nachf.. (context) - Selten - 1965
10 Value-function reinforcement learning in markov games (context) - Littman - 2000
4 Learning in the iterated prisoner's dilemma (context) - Sandholm, Crites - 1995
[Article contains additional citations not shown here]
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://books.nips.cc/nips15.html): More
Learning Attractor Landscapes for Learning Motor Primitives - Ijspeert, Nakanishi, Schaal (2003) (Correct)
A Statistical Mechanics Approach to Approximate Analytical.. - Malzahn, Opper (2003) (Correct)
Going Metric: Denoising Pairwise Data - Roth, Laub, Buhmann, Müller (2002) (Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC