MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs
نویسندگان
چکیده
We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partiallyobservable Markov decision problems (DECPOMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multirobot coordination, network traffic control, or distributed resource allocation. Solving such problems effectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA* has significant advantages. We introduce an anytime variant of MAA* and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems.
منابع مشابه
Producing efficient error-bounded solutions for transition independent decentralized mdps
There has been substantial progress on algorithms for single-agent sequential decision making using partially observable Markov decision processes (POMDPs). A number of efficient algorithms for solving POMDPs share two desirable properties: error-bounds and fast convergence rates. Despite significant efforts, no algorithms for solving decentralized POMDPs benefit from these properties, leading ...
متن کاملSolving POMDPs by Searching in Policy Space
Most algorithms for solving POMDPs itera tively improve a value function that implic itly represents a policy and are said to search in value function space. This paper presents an approach to solving POMDPs that repre sents a policy explicitly as a finite-state con troller and iteratively improves the controller by search in policy space. Two related al gorithms illustrate this approach. ...
متن کاملOptimally Solving Dec-POMDPs as Continuous-State MDPs
Decentralized partially observable Markov decision processes (Dec-POMDPs) provide a general model for decision-making under uncertainty in decentralized settings, but are difficult to solve optimally (NEXP-Complete). As a new way of solving these problems, we introduce the idea of transforming a Dec-POMDP into a continuous-state deterministic MDPwith a piecewise-linear and convex value function...
متن کاملTowards Computing Optimal Policies for Decentralized POMDPs
The problem of deriving joint policies for a group of agents that maximze some joint reward function can be modelled as a decentralized partially observable Markov decision process (DEC-POMDP). Significant algorithms have been developed for single agent POMDPs however, with a few exceptions, effective algorithms for deriving policies for decentralized POMDPS have not been developed. As a first ...
متن کاملThe Cross-Entropy Method for Policy Search in Decentralized POMDPs
Decentralized POMDPs (Dec-POMDPs) are becoming increasingly popular as models for multiagent planning under uncertainty, but solving a Dec-POMDP exactly is known to be an intractable combinatorial optimization problem. In this paper we apply the Cross-Entropy (CE) method, a recently introduced method for combinatorial optimization, to Dec-POMDPs, resulting in a randomized (sampling-based) algor...
متن کامل