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97
The Complexity of Decentralized Control of Markov Decision Processes
 Mathematics of Operations Research
, 2000
"... We consider decentralized control of Markov decision processes and give complexity bounds on the worstcase running time for algorithms that find optimal solutions. Generalizations of both the fullyobservable case and the partiallyobservable case that allow for decentralized control are described. ..."
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Cited by 288 (46 self)
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We consider decentralized control of Markov decision processes and give complexity bounds on the worstcase running time for algorithms that find optimal solutions. Generalizations of both the fullyobservable case and the partiallyobservable case that allow for decentralized control are described. For even two agents, the finitehorizon problems corresponding to both of these models are hard for nondeterministic exponential time. These complexity results illustrate a fundamental difference between centralized and decentralized control of Markov decision processes. In contrast to the problems involving centralized control, the problems we consider provably do not admit polynomialtime algorithms. Furthermore, assuming EXP NEXP, the problems require superexponential time to solve in the worst case.
The Communicative Multiagent Team Decision Problem: Analyzing Teamwork Theories and Models
 Journal of Artificial Intelligence Research
, 2002
"... Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Without a characterization of the optimalitycomplexity tradeoffs, it is impossible to determine whether the assumptions and app ..."
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Cited by 183 (22 self)
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Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Without a characterization of the optimalitycomplexity tradeoffs, it is impossible to determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for use by multiagent researchers in evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COMMTDP). The COMMTDP model combines and extends existing multiagent theories, such as decentralized partially observable Markov decision processes and economic team theory. In addition to their generality of representation, COMMTDPs also support the analysis of both the optimality of team performance and the computational complexity of the agents' decision problem. In analyzing complexity, we present a breakdown of the computational complexity of constructing optimal teams under various classes of problem domains, along the dimensions of observability and communication cost. In analyzing optimality, we exploit the COMMTDP's ability to encode existing teamwork theories and models to encode two instantiations of joint intentions theory taken from the literature. Furthermore, the COMMTDP model provides a basis for the development of novel team coordination algorithms. We derive a domainindependent criterion for optimal communication and provide a comparative analysis of the two joint intentions instantiations with respect to this optimal policy. We have implemented a reusable, domainindependent software package based on COMMTDPs to analyze teamwork coordination strategies, and we demons...
Taming Decentralized POMDPs: Towards Efficient Policy Computation for Multiagent Settings
 In IJCAI
, 2003
"... The problem of deriving joint policies for a group of agents that maximize some joint reward function can be modeled as a decentralized partially observable Markov decision process (POMDP). ..."
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Cited by 155 (23 self)
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The problem of deriving joint policies for a group of agents that maximize some joint reward function can be modeled as a decentralized partially observable Markov decision process (POMDP).
Infinitehorizon policygradient estimation
 Journal of Artificial Intelligence Research
, 2001
"... Gradientbased approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in valuefunction methods. In this paper we introduce � � , a si ..."
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Cited by 153 (5 self)
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Gradientbased approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in valuefunction methods. In this paper we introduce � � , a simulationbased algorithm for generating a biased estimate of the gradient of the average reward in Partially Observable Markov Decision Processes ( � s) controlled by parameterized stochastic policies. A similar algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm’s chief advantages are that it requires storage of only twice the number of policy parameters, uses one free parameter � � (which has a natural interpretation in terms of biasvariance tradeoff), and requires no knowledge of the underlying state. We prove convergence of � � , and show how the correct choice of the parameter is related to the mixing time of the controlled �. We briefly describe extensions of � � to controlled Markov chains, continuous state, observation and control spaces, multipleagents, higherorder derivatives, and a version for training stochastic policies with internal states. In a companion paper (Baxter, Bartlett, & Weaver, 2001) we show how the gradient estimates generated by � � can be used in both a traditional stochastic gradient algorithm and a conjugategradient procedure to find local optima of the average reward. 1.
Multiagent Planning with Factored MDPs
 In NIPS14
, 2001
"... We present a principled and efficient planning algorithm for cooperative multiagent dynamic systems. A striking feature of our method is that the coordination and communication between the agents is not imposed, but derived directly from the system dynamics and function approximation architecture ..."
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Cited by 145 (16 self)
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We present a principled and efficient planning algorithm for cooperative multiagent dynamic systems. A striking feature of our method is that the coordination and communication between the agents is not imposed, but derived directly from the system dynamics and function approximation architecture. We view the entire multiagent system as a single, large Markov decision process (MDP), which we assume can be represented in a factored way using a dynamic Bayesian network (DBN). The action space of the resulting MDP is the joint action space of the entire set of agents. Our approach is based on the use of factored linear value functions as an approximation to the joint value function. This factorization of the value function allows the agents to coordinate their actions at runtime using a natural message passing scheme. We provide a simple and efficient method for computing such an approximate value function by solving a single linear program, whose size is determined by the interaction between the value function structure and the DBN. We thereby avoid the exponential blowup in the state and action space. We show that our approach compares favorably with approaches based on reward sharing. We also show that our algorithm is an efficient alternative to more complicated algorithms even in the single agent case.
Dynamic Programming for Partially Observable Stochastic Games
 IN PROCEEDINGS OF THE NINETEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2004
"... We develop an exact dynamic programming algorithm for partially observable stochastic games (POSGs). The algorithm is a synthesis of dynamic programming for partially observable Markov decision processes (POMDPs) and iterated elimination of dominated strategies in normal form games. ..."
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Cited by 119 (23 self)
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We develop an exact dynamic programming algorithm for partially observable stochastic games (POSGs). The algorithm is a synthesis of dynamic programming for partially observable Markov decision processes (POMDPs) and iterated elimination of dominated strategies in normal form games.
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 117 (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
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 95 (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.
Solving transition independent decentralized Markov decision processes
 JAIR
, 2004
"... Formal treatment of collaborative multiagent systems has been lagging behind the rapid progress in sequential decision making by individual agents. Recent work in the area of decentralized Markov Decision Processes (MDPs) has contributed to closing this gap, but the computational complexity of thes ..."
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Cited by 74 (11 self)
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Formal treatment of collaborative multiagent systems has been lagging behind the rapid progress in sequential decision making by individual agents. Recent work in the area of decentralized Markov Decision Processes (MDPs) has contributed to closing this gap, but the computational complexity of these models remains a serious obstacle. To overcome this complexity barrier, we identify a specific class of decentralized MDPs in which the agents ’ transitions are independent. The class consists of independent collaborating agents that are tied together through a structured global reward function that depends on all of their histories of states and actions. We present a novel algorithm for solving this class of problems and examine its properties, both as an optimal algorithm and as an anytime algorithm. To the best of our knowledge, this is the first algorithm to optimally solve a nontrivial subclass of decentralized MDPs. It lays the foundation for further work in this area on both exact and approximate algorithms. 1.
Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs
 In Proc. of Int. Joint Conference on Autonomous Agents and Multi Agent Systems
, 2004
"... Partially observable decentralized decision making in robot teams is fundamentally different from decision making in fully observable problems. Team members cannot simply apply singleagent solution techniques in parallel. Instead, we must turn to game theoretic frameworks to correctly model the pro ..."
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Cited by 72 (1 self)
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Partially observable decentralized decision making in robot teams is fundamentally different from decision making in fully observable problems. Team members cannot simply apply singleagent solution techniques in parallel. Instead, we must turn to game theoretic frameworks to correctly model the problem. While partially observable stochastic games (POSGs) provide a solution model for decentralized robot teams, this model quickly becomes intractable. We propose an algorithm that approximates POSGs as a series of smaller, related Bayesian games, using heuristics such as QMDP to provide the future discounted value of actions. This algorithm trades off limited lookahead in uncertainty for computational feasibility, and results in policies that are locally optimal with respect to the selected heuristic. Empirical results are provided for both a simple problem for which the full POSG can also be constructed, as well as more complex, robotinspired, problems.