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Filtering by: Creator Kearns, Richard Seng Advisor Tadepalli, Prasad Rights Statement In Copyright

Planning in multi-agent partially observable domains using sparse sampling

Creator:

Donneau-Golencer, Thierry D.

Abstract:

A large number of sequential decision-making problems in uncertain environments can be modeled as Markov Decision Processes (MDPs). In such settings, an agent can observe at each time step the state of the environment and then executes an action, causing a stochastic transition to a new state of the environment...

Resource Type:

Masters Thesis
Bayesian methods for knowledge transfer and policy search in reinforcement learning

Creator:

Wilson, Aaron

Abstract:

How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. This dissertation focuses on Bayesian methods of representing learned knowledge and develops novel algorithms that exploit the represented...

Resource Type:

Dissertation
Monte-Carlo planning for probabilistic domains

Creator:

Bjarnason, Ronald V.

Abstract:

This thesis presents a progression of novel planning algorithms that culminates in a new family of diverse Monte-Carlo methods for probabilistic planning domains. We provide a proof for performance guarantees and analyze how these algorithms can resolve some of the shortcomings of traditional probabilistic planning methods. The direct policy search...

Resource Type:

Dissertation
Structured gradient boosting

Creator:

Parker, Charles (Charles Lincoln)

Abstract:

The goal of many machine learning problems can be formalized as the creation of a function that can properly classify an input vector, given a set of examples of that function. While this formalism has produced a number of success stories, there are notable situations in which it fails. One...

Resource Type:

Dissertation