217 research outputs found
Optimal and Approximate Q-value Functions for Decentralized POMDPs
Decision-theoretic planning is a popular approach to sequential decision
making problems, because it treats uncertainty in sensing and acting in a
principled way. In single-agent frameworks like MDPs and POMDPs, planning can
be carried out by resorting to Q-value functions: an optimal Q-value function
Q* is computed in a recursive manner by dynamic programming, and then an
optimal policy is extracted from Q*. In this paper we study whether similar
Q-value functions can be defined for decentralized POMDP models (Dec-POMDPs),
and how policies can be extracted from such value functions. We define two
forms of the optimal Q-value function for Dec-POMDPs: one that gives a
normative description as the Q-value function of an optimal pure joint policy
and another one that is sequentially rational and thus gives a recipe for
computation. This computation, however, is infeasible for all but the smallest
problems. Therefore, we analyze various approximate Q-value functions that
allow for efficient computation. We describe how they relate, and we prove that
they all provide an upper bound to the optimal Q-value function Q*. Finally,
unifying some previous approaches for solving Dec-POMDPs, we describe a family
of algorithms for extracting policies from such Q-value functions, and perform
an experimental evaluation on existing test problems, including a new
firefighting benchmark problem
Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions
A multi-agent partially observable Markov decision process (MPOMDP) is a
modeling paradigm used for high-level planning of heterogeneous autonomous
agents subject to uncertainty and partial observation. Despite their modeling
efficiency, MPOMDPs have not received significant attention in safety-critical
settings. In this paper, we use barrier functions to design policies for
MPOMDPs that ensure safety. Notably, our method does not rely on discretization
of the belief space, or finite memory. To this end, we formulate sufficient and
necessary conditions for the safety of a given set based on discrete-time
barrier functions (DTBFs) and we demonstrate that our formulation also allows
for Boolean compositions of DTBFs for representing more complicated safe sets.
We show that the proposed method can be implemented online by a sequence of
one-step greedy algorithms as a standalone safe controller or as a
safety-filter given a nominal planning policy. We illustrate the efficiency of
the proposed methodology based on DTBFs using a high-fidelity simulation of
heterogeneous robots.Comment: 8 pages and 4 figure
Dec-POMDPs as Non-Observable MDPs
A recent insight in the field of decentralized partially observable Markov decision processes (Dec-POMDPs) is that it is possible to convert a Dec-POMDP to a non-observable MDP, which is a special case of POMDP. This technical report provides an overview of this reduction and pointers to related literature
Applications of DEC-MDPs in multi-robot systems
International audienceOptimizing the operation of cooperative multi-robot systems that can cooperatively act in large and complex environments has become an important focal area of research. This issue is motivated by many applications involving a set of cooperative robots that have to decide in a decentralized way how to execute a large set of tasks in partially observable and uncertain environments. Such decision problems are encountered while developing exploration rovers, teams of patrolling robots, rescue-robot colonies, mine-clearance robots, et cetera.In this chapter, we introduce problematics related to the decentralized control of multi-robot systems. We rst describe some applicative domains and review the main characteristics of the decision problems the robots must deal with. Then, we review some existing approaches to solve problems of multiagent decen- tralized control in stochastic environments. We present the Decentralized Markov Decision Processes and discuss their applicability to real-world multi-robot applications. Then, we introduce OC-DEC-MDPs and 2V-DEC-MDPs which have been developed to increase the applicability of DEC-MDPs
Solving Common-Payoff Games with Approximate Policy Iteration
For artificially intelligent learning systems to have widespread
applicability in real-world settings, it is important that they be able to
operate decentrally. Unfortunately, decentralized control is difficult --
computing even an epsilon-optimal joint policy is a NEXP complete problem.
Nevertheless, a recently rediscovered insight -- that a team of agents can
coordinate via common knowledge -- has given rise to algorithms capable of
finding optimal joint policies in small common-payoff games. The Bayesian
action decoder (BAD) leverages this insight and deep reinforcement learning to
scale to games as large as two-player Hanabi. However, the approximations it
uses to do so prevent it from discovering optimal joint policies even in games
small enough to brute force optimal solutions. This work proposes CAPI, a novel
algorithm which, like BAD, combines common knowledge with deep reinforcement
learning. However, unlike BAD, CAPI prioritizes the propensity to discover
optimal joint policies over scalability. While this choice precludes CAPI from
scaling to games as large as Hanabi, empirical results demonstrate that, on the
games to which CAPI does scale, it is capable of discovering optimal joint
policies even when other modern multi-agent reinforcement learning algorithms
are unable to do so. Code is available at https://github.com/ssokota/capi .Comment: AAAI 202
Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions
A multi-agent partially observable Markov decision process (MPOMDP) is a modeling paradigm used for high-level planning of heterogeneous autonomous agents subject to uncertainty and partial observation. Despite their modeling efficiency, MPOMDPs have not received significant attention in safety-critical settings. In this paper, we use barrier functions to design policies for MPOMDPs that ensure safety. Notably, our method does not rely on discretizations of the belief space, or finite memory. To this end, we formulate sufficient and necessary conditions for the safety of a given set based on discrete-time barrier functions (DTBFs) and we demonstrate that our formulation also allows for Boolean compositions of DTBFs for representing more complicated safe sets. We show that the proposed method can be implemented online by a sequence of one-step greedy algorithms as a standalone safe controller or as a safety-filter given a nominal planning policy. We illustrate the efficiency of the proposed methodology based on DTBFs using a high-fidelity simulation of heterogeneous robots
A Survey on Sensor Networks from a Multiagent Perspective
Sensor networks (SNs) have arisen as one of the most promising technologies for the next decades. The recent emergence of small and inexpensive sensors based upon microelectromechanical systems ease the development and proliferation of this kind of networks in a wide range of actual-world applications. Multiagent systems (MAS) have been identified as one of the most suitable technologies to contribute to the deployment of SNs that exhibit flexibility, robustness and autonomy. The purpose of this survey is 2-fold. On the one hand, we review the most relevant contributions of agent technologies to this emerging application domain. On the other hand, we identify the challenges that researchers must address to establish MAS as the key enabling technology for SNs.This work has been funded by projects IEA(TIN2006-15662-C02-01), Agreement Technologies (CONSOLIDER CSD2007-0022, INGENIO 2010), EVE (TIN2009-14702-C02-01,TIN2009-14702-C02-02) and Generalitat de Catalunya under the gran
t2009-SGR-1434. Meritxell Vinyals is supported by the Spanish Ministry of Education (FPU grant AP2006-04636)Peer Reviewe
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