91 research outputs found
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
This paper presents a data-driven approach for multi-robot coordination in
partially-observable domains based on Decentralized Partially Observable Markov
Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a
general framework for cooperative sequential decision making under uncertainty
and MAs allow temporally extended and asynchronous action execution. To date,
most methods assume the underlying Dec-POMDP model is known a priori or a full
simulator is available during planning time. Previous methods which aim to
address these issues suffer from local optimality and sensitivity to initial
conditions. Additionally, few hardware demonstrations involving a large team of
heterogeneous robots and with long planning horizons exist. This work addresses
these gaps by proposing an iterative sampling based Expectation-Maximization
algorithm (iSEM) to learn polices using only trajectory data containing
observations, MAs, and rewards. Our experiments show the algorithm is able to
achieve better solution quality than the state-of-the-art learning-based
methods. We implement two variants of multi-robot Search and Rescue (SAR)
domains (with and without obstacles) on hardware to demonstrate the learned
policies can effectively control a team of distributed robots to cooperate in a
partially observable stochastic environment.Comment: Accepted to the 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2017
Stick-Breaking Policy Learning in Dec-POMDPs
Expectation maximization (EM) has recently been shown to be an efficient
algorithm for learning finite-state controllers (FSCs) in large decentralized
POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often
converge to maxima that are far from optimal. This paper considers a
variable-size FSC to represent the local policy of each agent. These
variable-size FSCs are constructed using a stick-breaking prior, leading to a
new framework called \emph{decentralized stick-breaking policy representation}
(Dec-SBPR). This approach learns the controller parameters with a variational
Bayesian algorithm without having to assume that the Dec-POMDP model is
available. The performance of Dec-SBPR is demonstrated on several benchmark
problems, showing that the algorithm scales to large problems while
outperforming other state-of-the-art methods
Probabilistic Inference Techniques for Scalable Multiagent Decision Making
Decentralized POMDPs provide an expressive framework for multiagent sequential decision making. However, the complexity of these models—NEXP-Complete even for two agents—has limited their scalability. We present a promising new class of approxima-tion algorithms by developing novel connections between multiagent planning and machine learning. We show how the multiagent planning problem can be reformulated as inference in a mixture of dynamic Bayesian networks (DBNs). This planning-as-inference approach paves the way for the application of efficient inference techniques in DBNs to multiagent decision making. To further improve scalability, we identify certain conditions that are sufficient to extend the approach to multiagent systems with dozens of agents. Specifically, we show that the necessary inference within the expectation-maximization framework can be decomposed into processes that often involve a small subset of agents, thereby facilitating scalability. We further show that a number of existing multiagent planning models satisfy these conditions. Experiments on large planning benchmarks confirm the benefits of our approach in terms of runtime and scalability with respect to existing techniques
Scalable Multiagent Planning using Probabilistic Inference
Multiagent planning has seen much progress with the development of formal models such as Dec-POMDPs. However, the complexity of these models—NEXP-Complete even for two agents— has limited scalability. We identify certain mild conditions that are sufficient to make multiagent planning amenable to a scalable approximation w.r.t. the number of agents. This is achieved by constructing a graphical model in which likelihood maximization is equivalent to plan optimization. Using the Expectation-Maximization framework for likelihood maximization, we show that the necessary inference can be decomposed into processes that often involve a small subset of agents, thereby facilitating scalability. We derive a global update rule that combines these local inferences to monotonically increase the overall solution quality. Experiments on a large multiagent planning benchmark confirm the benefits of the new approach in terms of runtime and scalability.
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
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