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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 ...
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 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 ...
Gaussian Mixture Belief Space Reinforcement Learning
(Georgia Institute of Technology, 2018-12)
In reinforcement learning and optimal control, one successful approach to address system stochasticity and epistemic uncertainty in the dynamics model is to consider, rather than a single state, a distribution over the ...
Co-evolution of shaping rewards and meta-parameters in reinforcement learning
(Georgia Institute of Technology, 2008-12)
In this article, we explore an evolutionary approach to the optimization of potential-based shaping rewards and meta-parameters in reinforcement learning. Shaping rewards is a frequently used approach to increase the ...
Human-like Action Segmentation for Option Learning
(Georgia Institute of Technology, 2011)
Robots learning interactively with a human partner has several open questions, one of which is increasing the efficiency of learning. One approach to this problem in the Reinforcement Learning domain is to use options, ...
A Multistrategy Case-Based and Reinforcement Learning Approach to Self-Improving Reactive Control Systems for Autonomous Robotic Navigation
(Georgia Institute of Technology, 1993)
This paper presents a self-improving reactive
control system for autonomous robotic navigation.
The navigation module uses a schema-based
reactive control system to perform the
navigation task. The learning module ...
Reward and Diversity in Multirobot Foraging
(Georgia Institute of Technology, 1999)
This research seeks to quantify the impact of the
choice of reward function on behavioral diversity in
learning robot teams. The methodology developed
for this work has been applied to multirobot foraging, soccer and ...
Learning of Parameter-Adaptive Reactive Controllers for Robotic Navigation
(Georgia Institute of Technology, 1997)
Reactive controllers are widely used in mobile robots because they are able to achieve successful performance in real-time. However, the configuration of a reactive controller depends highly on the operating conditions of ...