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pro vyhledávání: '"Nivison, Scott A"'
In this paper, we consider a transfer reinforcement learning problem involving agents with different action spaces. Specifically, for any new unseen task, the goal is to use a successful demonstration of this task by an expert agent in its action spa
Externí odkaz:
http://arxiv.org/abs/2410.14484
Autor:
Sivakumar, Kavinayan P., Shen, Yi, Bell, Zachary, Nivison, Scott, Chen, Boyuan, Zavlanos, Michael M.
In this paper, we study an inverse reinforcement learning problem that involves learning the reward function of a learning agent using trajectory data collected while this agent is learning its optimal policy. To address this problem, we propose an i
Externí odkaz:
http://arxiv.org/abs/2410.14135
Autor:
Wang, Zifan, Shen, Yi, Bell, Zachary I., Nivison, Scott, Zavlanos, Michael M., Johansson, Karl H.
We consider risk-averse learning in repeated unknown games where the goal of the agents is to minimize their individual risk of incurring significantly high cost. Specifically, the agents use the conditional value at risk (CVaR) as a risk measure and
Externí odkaz:
http://arxiv.org/abs/2209.02838
The objective of this research is to enable safety-critical systems to simultaneously learn and execute optimal control policies in a safe manner to achieve complex autonomy. Learning optimal policies via trial and error, i.e., traditional reinforcem
Externí odkaz:
http://arxiv.org/abs/2204.01409
The ability to learn and execute optimal control policies safely is critical to realization of complex autonomy, especially where task restarts are not available and/or the systems are safety-critical. Safety requirements are often expressed in terms
Externí odkaz:
http://arxiv.org/abs/2110.00271
We consider a zeroth-order distributed optimization problem, where the global objective function is a black-box function and, as such, its gradient information is inaccessible to the local agents. Instead, the local agents can only use the values of
Externí odkaz:
http://arxiv.org/abs/2109.13866
Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases. In genera
Externí odkaz:
http://arxiv.org/abs/2007.12666
We introduced a {\it working memory} augmented adaptive controller in our recent work. The controller uses attention to read from and write to the working memory. Attention allows the controller to read specific information that is relevant and updat
Externí odkaz:
http://arxiv.org/abs/1910.01189
Autor:
Makumi, Wanjiku A. *, Greene, Max L., Bell, Zachary I., Nivison, Scott, Kamalapurkar, Rushikesh, Dixon, Warren E. *
Publikováno v:
In IFAC PapersOnLine 2023 56(2):6871-6876
Akademický článek
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