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231
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
, 1998
"... In this paper, we adopt generalsum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zerosum stochastic games to a broader framework. We design a multiagent Qlearning method under this framework, and prove that it converges to a Na ..."
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Cited by 284 (4 self)
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In this paper, we adopt generalsum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zerosum stochastic games to a broader framework. We design a multiagent Qlearning method under this framework, and prove that it converges to a Nash equilibrium under specified conditions. This algorithm is useful for finding the optimal strategy when there exists a unique Nash equilibrium in the game. When there exist multiple Nash equilibria in the game, this algorithm should be combined with other learning techniques to find optimal strategies.
Multiagent Learning Using a Variable Learning Rate
 Artificial Intelligence
, 2002
"... Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents and so creates a situation of learning a moving target. Previous learning algorithms hav ..."
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Cited by 180 (8 self)
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Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents and so creates a situation of learning a moving target. Previous learning algorithms have one of two shortcomings depending on their approach. They either converge to a policy that may not be optimal against the specific opponents' policies, or they may not converge at all. In this article we examine this learning problem in the framework of stochastic games. We look at a number of previous learning algorithms showing how they fail at one of the above criteria. We then contribute a new reinforcement learning technique using a variable learning rate to overcome these shortcomings. Specifically, we introduce the WoLF principle, "Win or Learn Fast", for varying the learning rate. We examine this technique theoretically, proving convergence in selfplay on a restricted class of iterated matrix games. We also present empirical results on a variety of more general stochastic games, in situations of selfplay and otherwise, demonstrating the wide applicability of this method.
Learning to Cooperate via Policy Search
, 2000
"... Cooperative games are those in which both agents share the same payoff structure. Valuebased reinforcementlearning algorithms, such as variants of Qlearning, have been applied to learning cooperative games, but they only apply when the game state is completely observable to both agents. Poli ..."
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Cited by 131 (4 self)
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Cooperative games are those in which both agents share the same payoff structure. Valuebased reinforcementlearning algorithms, such as variants of Qlearning, have been applied to learning cooperative games, but they only apply when the game state is completely observable to both agents. Policy search methods are a reasonable alternative to valuebased methods for partially observable environments. In this paper, we provide a gradientbased distributed policysearch method for cooperative games and compare the notion of local optimum to that of Nash equilibrium. We demonstrate the effectiveness of this method experimentally in a small, partially observable simulated soccer domain. 1 INTRODUCTION The interaction of decision makers who share an environment is traditionally studied in game theory and economics. The game theoretic formalism is very general, and analyzes the problem in terms of solution concepts such as Nash equilibrium [12], but usually works under the assu...
Cooperative MultiAgent Learning: The State of the Art
 Autonomous Agents and MultiAgent Systems
, 2005
"... Cooperative multiagent systems are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the agents, multiagent problem complexity can rise rapidly with the number of agents or their behavioral sophistication. ..."
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Cited by 113 (6 self)
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Cooperative multiagent systems are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the agents, multiagent problem complexity can rise rapidly with the number of agents or their behavioral sophistication. The challenge this presents to the task of programming solutions to multiagent systems problems has spawned increasing interest in machine learning techniques to automate the search and optimization process. We provide a broad survey of the cooperative multiagent learning literature. Previous surveys of this area have largely focused on issues common to specific subareas (for example, reinforcement learning or robotics). In this survey we attempt to draw from multiagent learning work in a spectrum of areas, including reinforcement learning, evolutionary computation, game theory, complex systems, agent modeling, and robotics. We find that this broad view leads to a division of the work into two categories, each with its own special issues: applying a single learner to discover joint solutions to multiagent problems (team learning), or using multiple simultaneous learners, often one per agent (concurrent learning). Additionally, we discuss direct and indirect communication in connection with learning, plus open issues in task decomposition, scalability, and adaptive dynamics. We conclude with a presentation of multiagent learning problem domains, and a list of multiagent learning resources. 1
Nash QLearning for GeneralSum Stochastic Games
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2003
"... We extend Qlearning to a noncooperative multiagent context, using the framework of generalsum stochastic games. A learning agent maintains Qfunctions over joint actions, and performs updates based on assuming Nash equilibrium behavior over the current Qvalues. This learning protocol provably conv ..."
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Cited by 108 (0 self)
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We extend Qlearning to a noncooperative multiagent context, using the framework of generalsum stochastic games. A learning agent maintains Qfunctions over joint actions, and performs updates based on assuming Nash equilibrium behavior over the current Qvalues. This learning protocol provably converges given certain restrictions on the stage games (defined by Qvalues) that arise during learning. Experiments with a pair of twoplayer grid games suggest that such restrictions on the game structure are not necessarily required. Stage games encountered during learning in both grid environments violate the conditions. However, learning consistently converges in the first grid game, which has a unique equilibrium Qfunction, but sometimes fails to converge in the second, which has three different equilibrium Qfunctions. In a comparison of offline learning performance in both games, we find agents are more likely to reach a joint optimal path with Nash Qlearning than with a singleagent Qlearning method. When at least one agent adopts Nash Qlearning, the performance of both agents is better than using singleagent Qlearning. We have also implemented an online version of Nash Qlearning that balances exploration with exploitation, yielding improved performance.
Programmable reinforcement learning agents
, 2001
"... We present an expressive agent design language for reinforcement learning that allows the user to constrain the policies considered by the learning process.The language includes standard features such as parameterized subroutines, temporary interrupts, aborts, and memory variables, but also allows f ..."
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Cited by 102 (1 self)
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We present an expressive agent design language for reinforcement learning that allows the user to constrain the policies considered by the learning process.The language includes standard features such as parameterized subroutines, temporary interrupts, aborts, and memory variables, but also allows for unspecified choices in the agent program. For learning that which isn’t specified, we present provably convergent learning algorithms. We demonstrate by example that agent programs written in the language are concise as well as modular. This facilitates state abstraction and the transferability of learned skills. 1
Optimizing Information Exchange in Cooperative Multiagent Systems
, 2003
"... Decentralized control of a cooperative multiagent system is the problem faced by multiple decisionmakers that share a common set of objectives. The decisionmakers may be robots placed at separate geographical locations or computational processes distributed in an information space. It may be impo ..."
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Cited by 94 (17 self)
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Decentralized control of a cooperative multiagent system is the problem faced by multiple decisionmakers that share a common set of objectives. The decisionmakers may be robots placed at separate geographical locations or computational processes distributed in an information space. It may be impossible or undesirable for these decisionmakers to share all their knowledge all the time. Furthermore, exchanging information may incur a cost associated with the required bandwidth or with the risk of revealing it to competing agents. Assuming that communication may not be reliable adds another dimension of complexity to the problem. This paper develops a decisiontheoretic solution to this problem, treating both standard actions and communication as explicit choices that the decision maker must consider. The goal is to derive both action policies and communication policies that together optimize a global value function. We present an analytical model to evaluate the tradeo# between the cost of communication and the value of the information received. Finally, to address the complexity of this hard optimization problem, we develop a practical approximation technique based on myopic metalevel control of communication.
Rational and Convergent Learning in Stochastic Games
, 2001
"... 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 e ..."
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Cited by 82 (5 self)
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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.
AWESOME: A general multiagent learning algorithm that converges in selfplay and learns a best response against stationary opponents
, 2003
"... A satisfactory multiagent learning algorithm should, at a minimum, learn to play optimally against stationary opponents and converge to a Nash equilibrium in selfplay. The algorithm that has come closest, WoLFIGA, has been proven to have these two properties in 2player 2action repeated games— as ..."
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Cited by 81 (5 self)
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A satisfactory multiagent learning algorithm should, at a minimum, learn to play optimally against stationary opponents and converge to a Nash equilibrium in selfplay. The algorithm that has come closest, WoLFIGA, has been proven to have these two properties in 2player 2action repeated games— assuming that the opponent’s (mixed) strategy is observable. In this paper we present AWESOME, the first algorithm that is guaranteed to have these two properties in all repeated (finite) games. It requires only that the other players ’ actual actions (not their strategies) can be observed at each step. It also learns to play optimally against opponents that eventually become stationary. The basic idea behind AWESOME (Adapt When Everybody is Stationary, Otherwise Move to Equilibrium) is to try to adapt to the others’ strategies when they appear stationary, but otherwise to retreat to a precomputed equilibrium strategy. The techniques used to prove the properties of AWESOME are fundamentally different from those used for previous algorithms, and may help in analyzing other multiagent learning algorithms also.