232 research outputs found
Expectation Optimization with Probabilistic Guarantees in POMDPs with Discounted-sum Objectives
Partially-observable Markov decision processes (POMDPs) with discounted-sum
payoff are a standard framework to model a wide range of problems related to
decision making under uncertainty. Traditionally, the goal has been to obtain
policies that optimize the expectation of the discounted-sum payoff. A key
drawback of the expectation measure is that even low probability events with
extreme payoff can significantly affect the expectation, and thus the obtained
policies are not necessarily risk-averse. An alternate approach is to optimize
the probability that the payoff is above a certain threshold, which allows
obtaining risk-averse policies, but ignores optimization of the expectation. We
consider the expectation optimization with probabilistic guarantee (EOPG)
problem, where the goal is to optimize the expectation ensuring that the payoff
is above a given threshold with at least a specified probability. We present
several results on the EOPG problem, including the first algorithm to solve it.Comment: Full version of a paper published at IJCAI/ECAI 201
Stochastic Shortest Path with Energy Constraints in POMDPs
We consider partially observable Markov decision processes (POMDPs) with a
set of target states and positive integer costs associated with every
transition. The traditional optimization objective (stochastic shortest path)
asks to minimize the expected total cost until the target set is reached. We
extend the traditional framework of POMDPs to model energy consumption, which
represents a hard constraint. The energy levels may increase and decrease with
transitions, and the hard constraint requires that the energy level must remain
positive in all steps till the target is reached. First, we present a novel
algorithm for solving POMDPs with energy levels, developing on existing POMDP
solvers and using RTDP as its main method. Our second contribution is related
to policy representation. For larger POMDP instances the policies computed by
existing solvers are too large to be understandable. We present an automated
procedure based on machine learning techniques that automatically extracts
important decisions of the policy allowing us to compute succinct human
readable policies. Finally, we show experimentally that our algorithm performs
well and computes succinct policies on a number of POMDP instances from the
literature that were naturally enhanced with energy levels.Comment: Technical report accompanying a paper published in proceedings of
AAMAS 201
Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes
Markov decision processes (MDPs) are the defacto frame-work for sequential
decision making in the presence ofstochastic uncertainty. A classical
optimization criterion forMDPs is to maximize the expected discounted-sum
pay-off, which ignores low probability catastrophic events withhighly negative
impact on the system. On the other hand,risk-averse policies require the
probability of undesirableevents to be below a given threshold, but they do not
accountfor optimization of the expected payoff. We consider MDPswith
discounted-sum payoff with failure states which repre-sent catastrophic
outcomes. The objective of risk-constrainedplanning is to maximize the expected
discounted-sum payoffamong risk-averse policies that ensure the probability to
en-counter a failure state is below a desired threshold. Our maincontribution
is an efficient risk-constrained planning algo-rithm that combines UCT-like
search with a predictor learnedthrough interaction with the MDP (in the style
of AlphaZero)and with a risk-constrained action selection via linear
pro-gramming. We demonstrate the effectiveness of our approachwith experiments
on classical MDPs from the literature, in-cluding benchmarks with an order of
10^6 states.Comment: Published on AAAI 202
Stochastic Finite State Control of POMDPs with LTL Specifications
Partially observable Markov decision processes (POMDPs) provide a modeling framework for autonomous decision making under uncertainty and imperfect sensing, e.g. robot manipulation and self-driving cars. However, optimal control of POMDPs is notoriously intractable. This paper considers the quantitative problem of synthesizing sub-optimal stochastic finite state controllers (sFSCs) for POMDPs such that the probability of satisfying a set of high-level specifications in terms of linear temporal logic (LTL) formulae is maximized. We begin by casting the latter problem into an optimization and use relaxations based on the Poisson equation and McCormick envelopes. Then, we propose an stochastic bounded policy iteration algorithm, leading to a controlled growth in sFSC size and an any time algorithm, where the performance of the controller improves with successive iterations, but can be stopped by the user based on time or memory considerations. We illustrate the proposed method by a robot navigation case study
Sample-based Search Methods for Bayes-Adaptive Planning
A fundamental issue for control is acting in the face of uncertainty about the environment. Amongst other things, this induces a trade-off between exploration and exploitation. A model-based Bayesian agent optimizes its return by maintaining a posterior distribution over possible environments, and considering all possible future paths. This optimization is equivalent to solving a Markov Decision Process (MDP) whose hyperstate comprises the agent's beliefs about the environment, as well as its current state in that environment. This corresponding process is called a Bayes-Adaptive MDP (BAMDP). Even for MDPs with only a few states, it is generally intractable to solve the corresponding BAMDP exactly. Various heuristics have been devised, but those that are computationally tractable often perform indifferently, whereas those that perform well are typically so expensive as to be applicable only in small domains with limited structure. Here, we develop new tractable methods for planning in BAMDPs based on recent advances in the solution to large MDPs and general partially observable MDPs. Our algorithms are sample-based, plan online in a way that is focused on the current belief, and, critically, avoid expensive belief updates during simulations. In discrete domains, we use Monte-Carlo tree search to search forward in an aggressive manner. The derived algorithm can scale to large MDPs and provably converges to the Bayes-optimal solution asymptotically. We then consider a more general class of simulation-based methods in which approximation methods can be employed to allow value function estimates to generalize between hyperstates during search. This allows us to tackle continuous domains. We validate our approach empirically in standard domains by comparison with existing approximations. Finally, we explore Bayes-adaptive planning in environments that are modelled by rich, non-parametric probabilistic models. We demonstrate that a fully Bayesian agent can be advantageous in the exploration of complex and even infinite, structured domains
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
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