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Filtering by: Creator Provant, Stephen Glenn Committee Member Tadepalli, Prasad

Evaluation of Parallel Monte Carlo Tree Search Algorithms in Python

Creator:

Jothi, Shankar

Abstract:

Monte-Carlo Tree Search (MCTS) is an online-planning algorithm for decision-theoretic planning in domains with stochastic and combinatorial structure. The general applicability of MCTS makes it an ideal first choice to investigate when developing planners for complex applications requiring automated control and planning. The first contribution of this thesis is to...

Resource Type:

Masters Thesis
Resource placement, data rearrangement, and Hamiltonian cycles in torus networks

Creator:

Bae, Myung Mun

Abstract:

Many parallel machines, both commercial and experimental, have been/are being designed with toroidal interconnection networks. For a given number of nodes, the torus has a relatively larger diameter, but better cost/performance tradeoffs, such as higher channel bandwidth, and lower node degree, when compared to the hypercube. Thus, the torus is...

Resource Type:

Dissertation
Learning with Partial Supervision for Clustering and Classification

Creator:

Pei, Yuanli

Abstract:

In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...

Resource Type:

Dissertation
Incorporating and Learning Behavior Constraints for Sequential Decision Making

Creator:

Pinto, Jervis

Abstract:

Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement learning (RL) can be helpful. However, current systems either force developers to encode knowledge in very specific forms (e.g., state-action features), or assume advanced RL...

Resource Type:

Dissertation