32,561 research outputs found
Active Coverage for PAC Reinforcement Learning
Collecting and leveraging data with good coverage properties plays a crucial
role in different aspects of reinforcement learning (RL), including reward-free
exploration and offline learning. However, the notion of "good coverage" really
depends on the application at hand, as data suitable for one context may not be
so for another. In this paper, we formalize the problem of active coverage in
episodic Markov decision processes (MDPs), where the goal is to interact with
the environment so as to fulfill given sampling requirements. This framework is
sufficiently flexible to specify any desired coverage property, making it
applicable to any problem that involves online exploration. Our main
contribution is an instance-dependent lower bound on the sample complexity of
active coverage and a simple game-theoretic algorithm, CovGame, that nearly
matches it. We then show that CovGame can be used as a building block to solve
different PAC RL tasks. In particular, we obtain a simple algorithm for PAC
reward-free exploration with an instance-dependent sample complexity that, in
certain MDPs which are "easy to explore", is lower than the minimax one. By
further coupling this exploration algorithm with a new technique to do implicit
eliminations in policy space, we obtain a computationally-efficient algorithm
for best-policy identification whose instance-dependent sample complexity
scales with gaps between policy values.Comment: Accepted at COLT 202
Toward Specification-Guided Active Mars Exploration for Cooperative Robot Teams
As a step towards achieving autonomy in space exploration missions, we consider a cooperative robotics system consisting of a copter and a rover. The goal of the copter is to explore an unknown environment so as to maximize knowledge about a science mission expressed in linear temporal logic that is to be executed by the rover. We model environmental uncertainty as a belief space Markov decision process and formulate the problem as a two-step stochastic dynamic program that we solve in a way that leverages the decomposed nature of the overall system. We demonstrate in simulations that the robot team makes intelligent decisions in the face of uncertainty
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
A Learning Theoretic Approach to Energy Harvesting Communication System Optimization
A point-to-point wireless communication system in which the transmitter is
equipped with an energy harvesting device and a rechargeable battery, is
studied. Both the energy and the data arrivals at the transmitter are modeled
as Markov processes. Delay-limited communication is considered assuming that
the underlying channel is block fading with memory, and the instantaneous
channel state information is available at both the transmitter and the
receiver. The expected total transmitted data during the transmitter's
activation time is maximized under three different sets of assumptions
regarding the information available at the transmitter about the underlying
stochastic processes. A learning theoretic approach is introduced, which does
not assume any a priori information on the Markov processes governing the
communication system. In addition, online and offline optimization problems are
studied for the same setting. Full statistical knowledge and causal information
on the realizations of the underlying stochastic processes are assumed in the
online optimization problem, while the offline optimization problem assumes
non-causal knowledge of the realizations in advance. Comparing the optimal
solutions in all three frameworks, the performance loss due to the lack of the
transmitter's information regarding the behaviors of the underlying Markov
processes is quantified
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