VOCE: Variational optimization with conservative estimation for offline safe reinforcement learning
Offline safe reinforcement learning (RL) algorithms promise to learn policies that satisfy
safety constraints directly in offline datasets without interacting with the environment. This …
safety constraints directly in offline datasets without interacting with the environment. This …
POCE: Primal Policy Optimization with Conservative Estimation for Multi-constraint Offline Reinforcement Learning
Multi-constraint offline reinforcement learning (RL) promises to learn policies that satisfy
both cumulative and state-wise costs from offline datasets. This arrangement provides an …
both cumulative and state-wise costs from offline datasets. This arrangement provides an …
Deep generative models for offline policy learning: Tutorial, survey, and perspectives on future directions
Deep generative models (DGMs) have demonstrated great success across various domains,
particularly in generating texts, images, and videos using models trained from offline data …
particularly in generating texts, images, and videos using models trained from offline data …
Hybrid residual multiexpert reinforcement learning for spatial scheduling of high-density parking lots
Industries, such as manufacturing, are accelerating their embrace of the metaverse to
achieve higher productivity, especially in complex industrial scheduling. In view of the …
achieve higher productivity, especially in complex industrial scheduling. In view of the …
Offline Reinforcement Learning With Behavior Value Regularization
Offline reinforcement learning (offline RL) aims to find task-solving policies from prerecorded
datasets without online environment interaction. It is unfortunate that extrapolation errors can …
datasets without online environment interaction. It is unfortunate that extrapolation errors can …
Preferred-Action-Optimized Diffusion Policies for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn optimal policies from previously collected
datasets. Recently, due to their powerful representational capabilities, diffusion models have …
datasets. Recently, due to their powerful representational capabilities, diffusion models have …