A review of safe reinforcement learning: Methods, theory and applications
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …
making tasks. However, safety concerns are raised during deploying RL in real-world …
Last-iterate convergent policy gradient primal-dual methods for constrained mdps
We study the problem of computing an optimal policy of an infinite-horizon discounted
constrained Markov decision process (constrained MDP). Despite the popularity of …
constrained Markov decision process (constrained MDP). Despite the popularity of …
Probabilistic constraint for safety-critical reinforcement learning
W Chen, D Subramanian… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
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 …
reinforcement learning (RL). Specifically, a safe policy or controller is one that, with high …
Safe pontryagin differentiable programming
Abstract We propose a Safe Pontryagin Differentiable Programming (Safe PDP)
methodology, which establishes a theoretical and algorithmic framework to solve a broad …
methodology, which establishes a theoretical and algorithmic framework to solve a broad …
A Review of Safe Reinforcement Learning: Methods, Theories and Applications
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …
making tasks. However, safety concerns are raised during deploying RL in real-world …
State-augmented learnable algorithms for resource management in wireless networks
N NaderiAlizadeh, M Eisen… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
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 …
cast as optimizing a network-wide utility function, subject to constraints on the long-term …
Resilient Constrained Reinforcement Learning
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 …
constraint specifications are not identified before training. It is challenging to identify …
Deterministic Policy Gradient Primal-Dual Methods for Continuous-Space Constrained MDPs
We study the problem of computing deterministic optimal policies for constrained Markov
decision processes (MDPs) with continuous state and action spaces, which are widely …
decision processes (MDPs) with continuous state and action spaces, which are widely …
Towards Cooperative Driving among Heterogeneous CAVs: A Safe Multi-Agent Reinforcement Learning Approach
With the advancement of Intelligent Transportation Systems and Vehicle-to-Everything
communication technologies, the future traffic scenario is anticipated to be a mixed …
communication technologies, the future traffic scenario is anticipated to be a mixed …
SCPO: Safe Reinforcement Learning with Safety Critic Policy Optimization
J Mhamed, S Gu - arXiv preprint arXiv:2311.00880, 2023 - arxiv.org
Incorporating safety is an essential prerequisite for broadening the practical applications of
reinforcement learning in real-world scenarios. To tackle this challenge, Constrained Markov …
reinforcement learning in real-world scenarios. To tackle this challenge, Constrained Markov …