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Emulation and imitation via perceptual goal specifications
(Georgia Institute of Technology, 2019-04-02)
This dissertation aims to demonstrate how perceptual goal specifications may be used as alternative representations for specifying domain-specific reward functions for reinforcement learning. The works outlined in this ...
Evaluating visual conversational agents via cooperative human-AI games
(Georgia Institute of Technology, 2019-04-26)
As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but ...
EvalAI: Evaluating AI systems at scale
(Georgia Institute of Technology, 2018-12-06)
Artificial Intelligence research has progressed tremendously in the last few years. There has been the introduction of several new multi-modal datasets and tasks due to which it is becoming much harder to compare new ...
Efficient and principled robot learning: Theory and algorithms
(Georgia Institute of Technology, 2020-01-07)
Roboticists have long envisioned fully-automated robots that can operate reliably in unstructured environments. This is an exciting but extremely difficult problem; in order to succeed, robots must reason about sequential ...
Manipulating state space distributions for sample-efficient imitation-learning
(Georgia Institute of Technology, 2020-03-16)
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 ...
Learning neural algorithms with graph structures
(Georgia Institute of Technology, 2020-01-13)
Graph structures, like syntax trees, social networks, and programs, are ubiquitous in many real world applications including knowledge graph inference, chemistry and social network analysis. Over the past several decades, ...
Towards tighter integration of machine learning and discrete optimization
(Georgia Institute of Technology, 2019-03-28)
Discrete Optimization algorithms underlie intelligent decision-making in a wide variety of domains. From airline fleet scheduling to data center resource management and matching in ride-sharing services, decisions are often ...
Policy-based exploration for efficient reinforcement learning
(Georgia Institute of Technology, 2020-04-25)
Reinforcement Learning (RL) is the field of research focused on solving sequential decision-making tasks modeled as Markov Decision Processes. Researchers have shown RL to be successful at solving a variety of problems ...
Learning over functions, distributions and dynamics via stochastic optimization
(Georgia Institute of Technology, 2018-07-27)
Machine learning has recently witnessed revolutionary success in a wide spectrum of domains. The learning objectives, model representation, and learning algorithms are important components of machine learning methods. To ...
Learning Nash equilibria in zero-sum stochastic games via entropy-regularized policy approximation
(Georgia Institute of Technology, 2020-07-27)
In this thesis, we explore the use of policy approximation for reducing the computational cost of learning Nash Equilibria in Multi-Agent Reinforcement Learning. Existing multi-agent reinforcement learning methods are ...