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Deep Learning to Learn
(Georgia Institute of Technology, 2018-08-20)
Reinforcement learning and imitation learning have seen success in many domains, including
autonomous helicopter flight, Atari, simulated locomotion, Go, robotic manipulation. However, sample
complexity of these methods ...
Learning Locomotion: From Simulation to Real World
(2021-09-01)
Deep Reinforcement Learning (DRL) holds the promise of designing complex robotic controllers automatically. In this talk, I will discuss two different approaches to apply deep reinforcement learning to learn locomotion ...
Compressed computation of good policies in large MDPs
(2021-03-10)
Markov decision processes (MDPs) is a minimalist framework to capture that many tasks require long-term plans and feedback due to noisy dynamics. Yet, as a result MDPs lack structure and as such planning and learning in ...
Global Optimality Guarantees for Policy Gradient Methods
(2020-03-11)
Policy gradients methods are perhaps the most widely used class of reinforcement learning algorithms. These methods apply to complex, poorly understood, control problems by performing stochastic gradient descent over a ...
ML@GT Lab presents LAB LIGHTNING TALKS 2020
(2020-12-04)
Labs affiliated with the Machine Learning Center at Georgia Tech (ML@GT) will have the opportunity to share their research interests, work, and unique aspects of their lab in three minutes or less to interested graduate ...
Towards a Theory of Representation Learning for Reinforcement Learning
(2021-09-15)
Provably sample-efficient reinforcement learning from rich observational inputs remains a key open challenge in research. While impressive recent advances have allowed the use of linear modelling while carrying out ...
The Statistical Foundations of Learning to Control
(2018-11-14)
Given the dramatic successes in machine learning and reinforcement learning over the past half decade, there has been a surge of interest in applying these techniques to continuous control problems in robotics and autonomous ...