2,192 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World
Research in Artificial Intelligence (AI) has focused mostly on two extremes:
either on small improvements in narrow AI domains, or on universal theoretical
frameworks which are usually uncomputable, incompatible with theories of
biological intelligence, or lack practical implementations. The goal of this
work is to combine the main advantages of the two: to follow a big picture
view, while providing a particular theory and its implementation. In contrast
with purely theoretical approaches, the resulting architecture should be usable
in realistic settings, but also form the core of a framework containing all the
basic mechanisms, into which it should be easier to integrate additional
required functionality.
In this paper, we present a novel, purposely simple, and interpretable
hierarchical architecture which combines multiple different mechanisms into one
system: unsupervised learning of a model of the world, learning the influence
of one's own actions on the world, model-based reinforcement learning,
hierarchical planning and plan execution, and symbolic/sub-symbolic integration
in general. The learned model is stored in the form of hierarchical
representations with the following properties: 1) they are increasingly more
abstract, but can retain details when needed, and 2) they are easy to
manipulate in their local and symbolic-like form, thus also allowing one to
observe the learning process at each level of abstraction. On all levels of the
system, the representation of the data can be interpreted in both a symbolic
and a sub-symbolic manner. This enables the architecture to learn efficiently
using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl
Influence-Optimistic Local Values for Multiagent Planning --- Extended Version
Recent years have seen the development of methods for multiagent planning
under uncertainty that scale to tens or even hundreds of agents. However, most
of these methods either make restrictive assumptions on the problem domain, or
provide approximate solutions without any guarantees on quality. Methods in the
former category typically build on heuristic search using upper bounds on the
value function. Unfortunately, no techniques exist to compute such upper bounds
for problems with non-factored value functions. To allow for meaningful
benchmarking through measurable quality guarantees on a very general class of
problems, this paper introduces a family of influence-optimistic upper bounds
for factored decentralized partially observable Markov decision processes
(Dec-POMDPs) that do not have factored value functions. Intuitively, we derive
bounds on very large multiagent planning problems by subdividing them in
sub-problems, and at each of these sub-problems making optimistic assumptions
with respect to the influence that will be exerted by the rest of the system.
We numerically compare the different upper bounds and demonstrate how we can
achieve a non-trivial guarantee that a heuristic solution for problems with
hundreds of agents is close to optimal. Furthermore, we provide evidence that
the upper bounds may improve the effectiveness of heuristic influence search,
and discuss further potential applications to multiagent planning.Comment: Long version of IJCAI 2015 paper (and extended abstract at AAMAS
2015
Scalable Planning and Learning for Multiagent POMDPs: Extended Version
Online, sample-based planning algorithms for POMDPs have shown great promise
in scaling to problems with large state spaces, but they become intractable for
large action and observation spaces. This is particularly problematic in
multiagent POMDPs where the action and observation space grows exponentially
with the number of agents. To combat this intractability, we propose a novel
scalable approach based on sample-based planning and factored value functions
that exploits structure present in many multiagent settings. This approach
applies not only in the planning case, but also in the Bayesian reinforcement
learning setting. Experimental results show that we are able to provide high
quality solutions to large multiagent planning and learning problems
Perseus: Randomized Point-based Value Iteration for POMDPs
Partially observable Markov decision processes (POMDPs) form an attractive
and principled framework for agent planning under uncertainty. Point-based
approximate techniques for POMDPs compute a policy based on a finite set of
points collected in advance from the agents belief space. We present a
randomized point-based value iteration algorithm called Perseus. The algorithm
performs approximate value backup stages, ensuring that in each backup stage
the value of each point in the belief set is improved; the key observation is
that a single backup may improve the value of many belief points. Contrary to
other point-based methods, Perseus backs up only a (randomly selected) subset
of points in the belief set, sufficient for improving the value of each belief
point in the set. We show how the same idea can be extended to dealing with
continuous action spaces. Experimental results show the potential of Perseus in
large scale POMDP problems
Learning scalable and transferable multi-robot/machine sequential assignment planning via graph embedding
Can the success of reinforcement learning methods for simple combinatorial
optimization problems be extended to multi-robot sequential assignment
planning? In addition to the challenge of achieving near-optimal performance in
large problems, transferability to an unseen number of robots and tasks is
another key challenge for real-world applications. In this paper, we suggest a
method that achieves the first success in both challenges for robot/machine
scheduling problems.
Our method comprises of three components. First, we show a robot scheduling
problem can be expressed as a random probabilistic graphical model (PGM). We
develop a mean-field inference method for random PGM and use it for Q-function
inference. Second, we show that transferability can be achieved by carefully
designing two-step sequential encoding of problem state. Third, we resolve the
computational scalability issue of fitted Q-iteration by suggesting a heuristic
auction-based Q-iteration fitting method enabled by transferability we
achieved.
We apply our method to discrete-time, discrete space problems (Multi-Robot
Reward Collection (MRRC)) and scalably achieve 97% optimality with
transferability. This optimality is maintained under stochastic contexts. By
extending our method to continuous time, continuous space formulation, we claim
to be the first learning-based method with scalable performance among
multi-machine scheduling problems; our method scalability achieves comparable
performance to popular metaheuristics in Identical parallel machine scheduling
(IPMS) problems
Q-CP: Learning Action Values for Cooperative Planning
Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q-CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance
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