D Ding, CY Wei, K Zhang… - Advances in Neural …, 2024 - proceedings.neurips.cc
We study the problem of computing an optimal policy of an infinite-horizon discounted constrained Markov decision process (constrained MDP). Despite the popularity of …
In this paper, we consider the problem of learning safe policies for probabilistic-constrained reinforcement learning (RL). Specifically, a safe policy or controller is one that, with high …
W Jin, S Mou, GJ Pappas - Advances in Neural Information …, 2021 - proceedings.neurips.cc
Abstract We propose a Safe Pontryagin Differentiable Programming (Safe PDP) methodology, which establishes a theoretical and algorithmic framework to solve a broad …
S Gu, L Yang, Y Du, G Chen, F Walter… - … on Pattern Analysis …, 2024 - ieeexplore.ieee.org
Reinforcement Learning (RL) has achieved tremendous success in many complex decision- making tasks. However, safety concerns are raised during deploying RL in real-world …
We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term …
D Ding, Z Huan, A Ribeiro - International Conference on …, 2024 - proceedings.mlr.press
We study a class of constrained reinforcement learning (RL) problems in which multiple constraint specifications are not identified before training. It is challenging to identify …
We study the problem of computing deterministic optimal policies for constrained Markov decision processes (MDPs) with continuous state and action spaces, which are widely …
Y Pan, J Lei, P Yi, L Guo, H Chen - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
With the advancement of Intelligent Transportation Systems and Vehicle-to-Everything communication technologies, the future traffic scenario is anticipated to be a mixed …
Incorporating safety is an essential prerequisite for broadening the practical applications of reinforcement learning in real-world scenarios. To tackle this challenge, Constrained Markov …