We consider reinforcement learning in the parameterized setup, where the model is known to belong to a parameterized family of Markov Decision Processes (MDPs). We further impose here the assumption that set of possible parameters is finite, and consider the discounted return. We propose an on-line algorithm for learning in such parameterized models, dubbed the Parameter Elimination (PEL) algor...

In this paper we investigate human exploration/exploitation behavior in sequential-decision making tasks. Previous studies have suggested that people are suboptimal at scheduling exploration, and heuristic decision strategies are better predictors of human choices than the optimal model. By incorporating more realistic assumptions about subject’s knowledge and limitations into models of belief ...

The design and analysis of complex systems need to determine suitable configurations for meeting requirement constraints. The Monotonic Indices Space (MIS) method is a useful approach for monotonic requirement space exploration. However, the method is highly time and memory-Consuming. Aiming to the problem of low efficiency of sequential MIS method, this paper introduces a coarse-grained parall...

In this paper we investigate human exploration/exploitation behavior in a sequential-decision making task. Previous studies have suggested that people are suboptimal at scheduling exploration, and heuristic decision strategies are better predictors of human choices than the optimal model. By incorporating more realistic assumptions about subject’s knowledge and limitations into models of belief...

Handwriting is individualistic. The uniqueness of shape and style of handwriting can be used to identify the significant features in authenticating the author of writing. Acquiring these significant features leads to an important research in Writer Identification domain. This paper is meant to explore the usage of feature selection in Writer Identification. Various filter and wrapper feature se...

Boltzmann exploration is a classic strategy for sequential decision-making under uncertainty, and is one of the most standard tools in Reinforcement Learning (RL). Despite its widespread use, there is virtually no theoretical understanding about the limitations or the actual benefits of this exploration scheme. Does it drive exploration in a meaningful way? Is it prone to misidentifying the opt...

Recent research in robot exploration and mapping has focused on sampling environmental hotspot fields. This exploration task is formalized by Low, Dolan, and Khosla (2008) in a sequential decision-theoretic planning under uncertainty framework called MASP. The time complexity of solving MASP approximately depends on the map resolution, which limits its use in large-scale, high-resolution explor...

Abstract The deposition and evolution of fine-grained sediments is a hot topic in sedimentary rock studies important for accurately evaluating shale gas sweet spots. In this paper, the characteristics Wufeng-Longmaxi shales, major targets Chinese exploration, were studied by using core observations, thin section analyses, scanning electron microscopy, geochemical analysis, fossil identification...

Sequential portfolio selection has attracted increasing interest in the machine learning and quantitative finance communities in recent years. As a mathematical framework for reinforcement learning policies, the stochastic multi-armed bandit problem addresses the primary difficulty in sequential decision-making under uncertainty, namely the exploration versus exploitation dilemma, and therefore...

The problem of coalition formation when agents are uncertain about the types or capabilities of their potential partners is a critical one. In [3] a Bayesian reinforcement learning framework is developed for this problem when coalitions are formed (and tasks undertaken) repeatedly: not only does the model allow agents to refine their beliefs about the types of others, but uses value of informat...