PhD Proposal by Yannick Schroecker

Event Details
  • Date/Time:
    • Friday February 8, 2019
      11:00 am - 1:00 pm
  • Location: CCB 347
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Summaries

Summary Sentence: Manipulating State Space Distributions for Sample-Efficient Imitation- and Reinforcement Learning

Full Summary: No summary paragraph submitted.

Title: Manipulating State Space Distributions for Sample-Efficient Imitation- and Reinforcement Learning

 

Yannick Schroecker

Ph.D. student in Computer Science

School of Interactive Computing

College of Computing

Georgia Institute of Technology

 

Date: Friday, February 8, 2019

Time: 11:00am-1:00 PM EST

Location: CCB 347

 

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Committee:

Dr. Charles Isbell (Advisor), School of Interactive Computing, Georgia Institute of Technology

Dr. Sonia Chernova, School of Interactive Computing, Georgia Institute of Technology

Dr. Byron Boots, School of Interactive Computing, Georgia Institute of Technology

Dr. Irfan Essa, School of Interactive Computing, Georgia Institute of Technology

Dr. Nando de Freitas, Department of Computer Science, University of Oxford and Google DeepMind

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Summary:

Imitation learning has emerged as one of the most effective approaches to train agents to act intelligently in unstructured and unknown domains. On its own or in combination with reinforcement learning, it enables agents to copy the expert's behavior and to solve complex, long-term decision making problems. However, to utilize demonstrations effectively and learn from a finite amount of data, the agent needs to develop an understanding of the environment. This thesis investigates estimators of the state-distribution gradient as a means to influence which states the agent will see and thereby guide it to imitate the expert's behavior. Furthermore, this thesis will show that approaches which reason over future states in this way are able to learn from sparse signals and thus provide a way to effectively program agents. Specifically, this talk proposes to validate the following thesis statement:

Exploiting inherent structure in Markov chain stationary distributions allows learning agents to reason about likely future observations and enables robust and interactive imitation learning, providing an efficient way to teach agents and provide guidance for composite learning systems.

 

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Graduate Studies

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Phd proposal
Status
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Feb 5, 2019 - 11:30am
  • Last Updated: Feb 5, 2019 - 11:30am