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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 ...
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 ...
Adding Machine Intelligence to Hybrid Memory Management
(Georgia Institute of Technology, 2021-07-30)
Computing platforms increasingly incorporate heterogeneous memory hardware technologies, as a way to scale application performance, memory capacities and achieve cost effectiveness. However, this heterogeneity, along with ...
Scaling solutions to Markov Decision Problems
(Georgia Institute of Technology, 2011-11-14)
The Markov Decision Problem (MDP) is a widely applied mathematical model useful for describing a wide array of real world decision problems ranging from navigation to scheduling to robotics. Existing methods for solving ...
Model-Based Reflection for Agent Evolution
(Georgia Institute of Technology, 2000)
Adaptability is a key characteristic of intelligence. My research explores techniques
for enabling software agents to adapt themselves as their functional requirements
change incrementally. In the domain of manufacturing, ...
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 ...
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 ...
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 ...
Physics-based reinforcement learning for autonomous manipulation
(Georgia Institute of Technology, 2015-08-21)
With recent research advances, the dream of bringing domestic robots into our everyday lives has become more plausible than ever. Domestic robotics has grown dramatically in the past decade, with applications ranging from ...
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 ...