6,545 research outputs found
Evolutionary Policy Iteration for Solving Markov Decision Processes
We propose a novel algorithm called Evolutionary Policy Iteration (EPI) for solving infinite horizon discounted reward Markov Decision Process (MDP) problems. EPI inherits the spirit of the well-known PI algorithm but eliminates the need to maximize over the entire action space in the policy improvement step, so it should be most effective for problems with very large action spaces. EPI iteratively generates a "population" or a set of policies such that the performance of the "elite policy" for a population is monotonically improved with respect to a defined fitness function. EPI converges with probability one to a population whose elite policy is an optimal policy for a given MDP. EPI is naturally parallelizable and along this discussion, a distributed variant of PI is also studied
Multi-Objective Approaches to Markov Decision Processes with Uncertain Transition Parameters
Markov decision processes (MDPs) are a popular model for performance analysis
and optimization of stochastic systems. The parameters of stochastic behavior
of MDPs are estimates from empirical observations of a system; their values are
not known precisely. Different types of MDPs with uncertain, imprecise or
bounded transition rates or probabilities and rewards exist in the literature.
Commonly, analysis of models with uncertainties amounts to searching for the
most robust policy which means that the goal is to generate a policy with the
greatest lower bound on performance (or, symmetrically, the lowest upper bound
on costs). However, hedging against an unlikely worst case may lead to losses
in other situations. In general, one is interested in policies that behave well
in all situations which results in a multi-objective view on decision making.
In this paper, we consider policies for the expected discounted reward
measure of MDPs with uncertain parameters. In particular, the approach is
defined for bounded-parameter MDPs (BMDPs) [8]. In this setting the worst, best
and average case performances of a policy are analyzed simultaneously, which
yields a multi-scenario multi-objective optimization problem. The paper
presents and evaluates approaches to compute the pure Pareto optimal policies
in the value vector space.Comment: 9 pages, 5 figures, preprint for VALUETOOLS 201
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
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