H-GAP: Humanoid Control with a Generalist Planner
Humanoid control is an important research challenge offering avenues for integration into
human-centric infrastructures and enabling physics-driven humanoid animations. The …
human-centric infrastructures and enabling physics-driven humanoid animations. The …
Transductive Reward Inference on Graph
In this study, we present a transductive inference approach on that reward information
propagation graph, which enables the effective estimation of rewards for unlabelled data in …
propagation graph, which enables the effective estimation of rewards for unlabelled data in …
CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn an effective policy from a pre-collected
dataset. Most existing works are to develop sophisticated learning algorithms, with less …
dataset. Most existing works are to develop sophisticated learning algorithms, with less …
Battery thermal management system optimization using Deep reinforced learning algorithm
H Cheng, S Jung, YB Kim - Applied Thermal Engineering, 2024 - Elsevier
The temperature of the battery plays a critical role in the working conditions and a suitable
temperature environment can help the battery extend its capacity. Air cooling and water …
temperature environment can help the battery extend its capacity. Air cooling and water …
[HTML][HTML] Trust region policy optimization via entropy regularization for Kullback–Leibler divergence constraint
Trust region policy optimization (TRPO) is one of the landmark policy optimization algorithms
in deep reinforcement learning. Its purpose is to maximize a surrogate objective based on …
in deep reinforcement learning. Its purpose is to maximize a surrogate objective based on …
Intelligent Cooperative Caching at Mobile Edge based on Offline Deep Reinforcement Learning
Cooperative edge caching enables edge servers to jointly utilize their cache to store popular
contents, thus drastically reducing the latency of content acquisition. One fundamental …
contents, thus drastically reducing the latency of content acquisition. One fundamental …
OCEAN-MBRL: Offline Conservative Exploration for Model-Based Offline Reinforcement Learning
Model-based offline reinforcement learning (RL) algorithms have emerged as a promising
paradigm for offline RL. These algorithms usually learn a dynamics model from a static …
paradigm for offline RL. These algorithms usually learn a dynamics model from a static …
Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?
Transformer-based trajectory optimization methods have demonstrated exceptional
performance in offline Reinforcement Learning (offline RL), yet it poses challenges due to …
performance in offline Reinforcement Learning (offline RL), yet it poses challenges due to …
Transfer Reinforcement Learning for Mixed Observability Markov Decision Processes with Time-Varying Interval-Valued Parameters and Its Application in Pandemic …
M Du, H Yu, N Kong - INFORMS Journal on Computing, 2024 - pubsonline.informs.org
We investigate a novel type of online sequential decision problem under uncertainty, namely
mixed observability Markov decision process with time-varying interval-valued parameters …
mixed observability Markov decision process with time-varying interval-valued parameters …
基于表征学习的离线强化学习方法研究综述
王雪松， 王荣荣， 程玉虎 - 自动化学报, 2024 - aas.net.cn
强化学习通过智能体与环境在线交互来学习最优策略, 近年来已成为解决复杂环境下感知决策
问题的重要手段. 然而, 在线收集数据的方式可能会引发安全, 时间或成本等问题 …
问题的重要手段. 然而, 在线收集数据的方式可能会引发安全, 时间或成本等问题 …