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 …
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 …
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 …
A dual approach to constrained markov decision processes with entropy regularization
We study entropy-regularized constrained Markov decision processes (CMDPs) under the
soft-max parameterization, in which an agent aims to maximize the entropy-regularized …
soft-max parameterization, in which an agent aims to maximize the entropy-regularized …
Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs
We study sequential decision making problems aimed at maximizing the expected total
reward while satisfying a constraint on the expected total utility. We employ the natural policy …
reward while satisfying a constraint on the expected total utility. We employ the natural policy …
Anchor-changing regularized natural policy gradient for multi-objective reinforcement learning
We study policy optimization for Markov decision processes (MDPs) with multiple reward
value functions, which are to be jointly optimized according to given criteria such as …
value functions, which are to be jointly optimized according to given criteria such as …
Finding correlated equilibrium of constrained Markov game: A primal-dual approach
Constrained Markov game is a fundamental problem that covers many applications, where
multiple players compete with each other under behavioral constraints. The existing …
multiple players compete with each other under behavioral constraints. The existing …
Adaptive User Interface Generation Through Reinforcement Learning: A Data-Driven Approach to Personalization and Optimization
Q Sun, Y Xue, Z Song - arxiv preprint arxiv:2412.16837, 2024 - arxiv.org
This study introduces an adaptive user interface generation technology, emphasizing the
role of Human-Computer Interaction (HCI) in optimizing user experience. By focusing on …
role of Human-Computer Interaction (HCI) in optimizing user experience. By focusing on …
Provably efficient generalized lagrangian policy optimization for safe multi-agent reinforcement learning
We examine online safe multi-agent reinforcement learning using constrained Markov
games in which agents compete by maximizing their expected total rewards under a …
games in which agents compete by maximizing their expected total rewards under a …
Stochastic optimization under hidden convexity
In this work, we consider constrained stochastic optimization problems under hidden
convexity, ie, those that admit a convex reformulation via non-linear (but invertible) map $ c …
convexity, ie, those that admit a convex reformulation via non-linear (but invertible) map $ c …