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On Thompson Sampling with Langevin Algorithms
Thompson sampling is a methodology for multi-armed bandit problems that ...
02/23/2020 ∙ by Eric Mazumdar, et al. ∙
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Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning
This paper proposes a framework for adaptively learning a feedback linea...
04/06/2020 ∙ by Tyler Westenbroek, et al. ∙
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Policy-Gradient Algorithms Have No Guarantees of Convergence in Continuous Action and State Multi-Agent Settings
We show by counterexample that policy-gradient algorithms have no guaran...
07/08/2019 ∙ by Eric Mazumdar, et al. ∙
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Feedback Linearization for Unknown Systems via Reinforcement Learning
We present a novel approach to control design for nonlinear systems, whi...
10/29/2019 ∙ by Tyler Westenbroek, et al. ∙
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Inverse Risk-Sensitive Reinforcement Learning
We address the problem of inverse reinforcement learning in Markov decis...
03/29/2017 ∙ by Lillian J. Ratliff, et al. ∙
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On the Convergence of Competitive, Multi-Agent Gradient-Based Learning
As learning algorithms are increasingly deployed in markets and other co...
04/16/2018 ∙ by Eric Mazumdar, et al. ∙
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Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings
Considering a class of gradient-based multi-agent learning algorithms in...
05/30/2019 ∙ by Benjamin Chasnov, et al. ∙
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Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in `Almost All' Zero-Sum Continuous Games
We prove that differential Nash equilibria are generic amongst local Nas...
02/03/2020 ∙ by Eric Mazumdar, et al. ∙
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