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Learning Roles: Behavioral Diversity in Robot Teams
(Georgia Institute of Technology, 1997)
This paper describes research investigating behavioral specialization in
learning robot teams. Each agent is provided a common set of skills (motor
schema-based behavioral assemblages) from which it builds a task-achie ...
Integrating reinforcement learning into a programming language
(Georgia Institute of Technology, 2017-06-26)
Reinforcement learning is a promising solution to the intelligent agent problem, namely, given the state of the world, which action should an agent take to maximize goal attainment. However, reinforcement learning algorithms ...
Learning dynamic processes over graphs
(Georgia Institute of Technology, 2020-07-09)
Graphs appear as a versatile representation of information across domains spanning social networks, biological networks, transportation networks, molecular structures, knowledge networks, web information network and many ...
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 to walk using deep reinforcement learning and transfer learning
(Georgia Institute of Technology, 2020-05-17)
We seek to develop computational tools to reproduce the locomotion of humans and animals in complex and unpredictable environments. Such tools can have significant impact in computer graphics, robotics, machine learning, ...
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 ...
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 ...
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, ...
Learning Rotation-in-Place and Orbiting Policies for a Quadruped Robot
(Georgia Institute of Technology, 2022-05)
Reinforcement learning (RL) algorithms have successfully learned control policies for quadruped locomotion such as walking, rotation, and basic navigation. We utilize Proximal Policy Optimization and iGibson to train a ...
Mastering Reconnaissance Blind Chess with Reinforcement Learning
(Georgia Institute of Technology, 2020-05)
Research within Artificial Intelligence has often set goals of being able to autonomously play games (e.g., Chess or Go) at or above human level. Novel machine learning-based agents have recently made advances in the ...