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Asymmetric multiagent reinforcement learning

by Ville Könönen , 2004
"... A novel model for asymmetric multiagent reinforcement learning is introduced in this paper. The model addresses the problem where the information states of the agents involved in the learning task are not equal; some agents (leaders) have information how their opponents (followers) will select thei ..."
Abstract - Cited by 17 (4 self) - Add to MetaCart
A novel model for asymmetric multiagent reinforcement learning is introduced in this paper. The model addresses the problem where the information states of the agents involved in the learning task are not equal; some agents (leaders) have information how their opponents (followers) will select

Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm

by Junling Hu, Michael P. Wellman , 1998
"... In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games to a broader framework. We design a multiagent Q-learning method under this framework, and prove that it converges to a Na ..."
Abstract - Cited by 331 (4 self) - Add to MetaCart
In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games to a broader framework. We design a multiagent Q-learning method under this framework, and prove that it converges to a

Hierarchical Multiagent Reinforcement Learning

by Mohammad Ghavamzadeh, Sridhar Mahadevan , 2004
"... In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multiagent tasks. We introduce a hierarchical multiagent reinforcement learning (RL) framework and propose a hierarchical multiagent RL algorithm called Cooperative HRL. In o ..."
Abstract - Cited by 23 (5 self) - Add to MetaCart
In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multiagent tasks. We introduce a hierarchical multiagent reinforcement learning (RL) framework and propose a hierarchical multiagent RL algorithm called Cooperative HRL

Countering Deception in Multiagent Reinforcement Learning

by Bikramjit Banerjee, Jing Peng - In Proceedings of the Sixth International Workshop on Trust, Privacy, Deception, and Fraud in Agent Societies , 2003
"... Multiagent Reinforcement Learning (MRL) is a growing area of research. What makes it particularly challenging is the non-stationarity of the target function. Most of the existing work in this area, however, address either stationary environments or self-play. We assume an asymmetric and non-stationa ..."
Abstract - Cited by 4 (1 self) - Add to MetaCart
Multiagent Reinforcement Learning (MRL) is a growing area of research. What makes it particularly challenging is the non-stationarity of the target function. Most of the existing work in this area, however, address either stationary environments or self-play. We assume an asymmetric and non

Multiagent Reinforcement Learning in Stochastic Games

by Junling Hu, Michael P. Wellman - Online]. Available: citeseer.ist.psu.edu/hu99multiagent.html , 1999
"... We adopt stochastic games as a general framework for dynamic noncooperative systems. This framework provides a way of describing the dynamic interactions of agents in terms of individuals' Markov decision processes. By studying this framework, we go beyond the common practice in the study of le ..."
Abstract - Cited by 8 (0 self) - Add to MetaCart
of learning in games, which primarily focus on repeated games or extensive-form games. For stochastic games with incomplete information, we design a multiagent reinforcement learning method which allows agents to learn Nash equilibrium strategies. We show in both theory and experiments that this algorithm

Advice Taking in Multiagent Reinforcement Learning

by Michael Rovatsos, Ros Belesiotis
"... This paper proposes the β-WoLF algorithm for multiagent reinforcement learning (MARL) in the stochastic games framework that uses an additional “advice ” signal to inform agents about mutually beneficial forms of behaviour. β-WoLF is an extension of the WoLF-PHC algorithm that allows agents to asses ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
This paper proposes the β-WoLF algorithm for multiagent reinforcement learning (MARL) in the stochastic games framework that uses an additional “advice ” signal to inform agents about mutually beneficial forms of behaviour. β-WoLF is an extension of the WoLF-PHC algorithm that allows agents

Empirically Evaluating Multiagent Reinforcement Learning Algorithms

by Asher Lipson, Kevin Leyton-brown, Nando De Freitas , 2005
"... This article makes two contributions. First, we present a platform for running and analyzing multiagent reinforcement learning experiments. Second, to demonstrate this platform we undertook and evaluated an empirical test of multiagent reinforcement learning algorithms from the literature, which to ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
This article makes two contributions. First, we present a platform for running and analyzing multiagent reinforcement learning experiments. Second, to demonstrate this platform we undertook and evaluated an empirical test of multiagent reinforcement learning algorithms from the literature, which

Implicit imitation in multiagent reinforcement learning

by Bob Price, Craig Boutilier - IN: PROC. ICML , 1999
"... Imitation is actively being studied as an effective means of learning in multi-agent environments. It allows an agent to learn how to act well (perhaps optimally) by passively observing the actions of cooperative teachers or other more experienced agents its environment. We propose a straightforward ..."
Abstract - Cited by 40 (3 self) - Add to MetaCart
Imitation is actively being studied as an effective means of learning in multi-agent environments. It allows an agent to learn how to act well (perhaps optimally) by passively observing the actions of cooperative teachers or other more experienced agents its environment. We propose a

Heuristic Selection of Actions in Multiagent Reinforcement Learning ∗

by Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro
"... This work presents a new algorithm, called Heuristically Accelerated Minimax-Q (HAMMQ), that allows the use of heuristics to speed up the wellknown Multiagent Reinforcement Learning algorithm Minimax-Q. A heuristic function H that influences the choice of the actions characterises the HAMMQ algorith ..."
Abstract - Cited by 13 (2 self) - Add to MetaCart
This work presents a new algorithm, called Heuristically Accelerated Minimax-Q (HAMMQ), that allows the use of heuristics to speed up the wellknown Multiagent Reinforcement Learning algorithm Minimax-Q. A heuristic function H that influences the choice of the actions characterises the HAMMQ

Coordination in Multiagent Reinforcement Learning: A Bayesian Approach

by Georgios Chalkiadakis, Craig Boutilier - In Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems , 2003
"... Much emphasis in multiagent reinforcement learning (MARL) research is placed on ensuring that MARL algorithms (eventually) converge to desirable equilibria. As in standard reinforcement learning, convergence generally requires sufficient exploration of strategy space. However, exploration often com ..."
Abstract - Cited by 66 (6 self) - Add to MetaCart
Much emphasis in multiagent reinforcement learning (MARL) research is placed on ensuring that MARL algorithms (eventually) converge to desirable equilibria. As in standard reinforcement learning, convergence generally requires sufficient exploration of strategy space. However, exploration often
Results 1 - 10 of 12,035