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pro vyhledávání: '"Jothimurugan, Kishor"'
Autor:
Alur, Rajeev, Bastani, Osbert, Jothimurugan, Kishor, Perez, Mateo, Somenzi, Fabio, Trivedi, Ashutosh
The difficulty of manually specifying reward functions has led to an interest in using linear temporal logic (LTL) to express objectives for reinforcement learning (RL). However, LTL has the downside that it is sensitive to small perturbations in the
Externí odkaz:
http://arxiv.org/abs/2305.17115
Publikováno v:
Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023
Compositional reinforcement learning is a promising approach for training policies to perform complex long-horizon tasks. Typically, a high-level task is decomposed into a sequence of subtasks and a separate policy is trained to perform each subtask.
Externí odkaz:
http://arxiv.org/abs/2302.02984
Reinforcement learning has been shown to be an effective strategy for automatically training policies for challenging control problems. Focusing on non-cooperative multi-agent systems, we propose a novel reinforcement learning framework for training
Externí odkaz:
http://arxiv.org/abs/2206.03348
Reactive synthesis algorithms allow automatic construction of policies to control an environment modeled as a Markov Decision Process (MDP) that are optimal with respect to high-level temporal logic specifications. However, they assume that the MDP m
Externí odkaz:
http://arxiv.org/abs/2111.00272
Publikováno v:
35th Conference on Neural Information Processing Systems (NeurIPS 2021)
We study the problem of learning control policies for complex tasks given by logical specifications. Recent approaches automatically generate a reward function from a given specification and use a suitable reinforcement learning algorithm to learn a
Externí odkaz:
http://arxiv.org/abs/2106.13906
We study regenerative stopping problems in which the system starts anew whenever the controller decides to stop and the long-term average cost is to be minimized. Traditional model-based solutions involve estimating the underlying process from data a
Externí odkaz:
http://arxiv.org/abs/2105.02318
Publikováno v:
PMLR: Volume 130 (AISTATS 2021)
We propose a novel hierarchical reinforcement learning framework for control with continuous state and action spaces. In our framework, the user specifies subgoal regions which are subsets of states; then, we (i) learn options that serve as transitio
Externí odkaz:
http://arxiv.org/abs/2010.15638
Publikováno v:
In Advances in Neural Information Processing Systems, pp. 13041-13051. 2019
Reinforcement learning is a promising approach for learning control policies for robot tasks. However, specifying complex tasks (e.g., with multiple objectives and safety constraints) can be challenging, since the user must design a reward function t
Externí odkaz:
http://arxiv.org/abs/2008.09293
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