为每一个task 单独训练一个policy network, 这个可以用来作为上限对比。 使用同一个网络,但是状态加入a one-hot task ID作为输入。 另外就是使用multi-head,也就是同一个backbone,后面输出有多个head,分别代表一个task输出。 之前同时nips2020,滴滴有一篇文章Multi-Task Deep Reinforcement Learning with Knowledge Tra...
Explainable Reinforcement Learning: A Survey. 2020 论文地址:Explainable Reinforcement Learning: A Survey 摘要:可解释的人工智能(XAI),也就是更透明和可解释的人工智能模型的发展,… 郭达森发表于机器学习方... 【简读】Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces 202...
Multi-task Deep Reinforcement Learning with PopArtMatteo Hessel Hubert Soyer Lasse EspeholtWojciech Czarnecki Simon Schmitt Hado van HasseltAbstractThe reinforcement learning community has made greatstrides in designing algorithms capable of exceeding humanperformance on specif ic tasks. These algorithms are m...
In this paper, we propose a deep reinforcement learning algorithm to learn multiple tasks concurrently. A new network architecture is proposed in the algorithm which reduces the number of parameters needed by more than 75% per task compared to typical single-task deep reinforcement learning ...
Vision-Based Robotic Object Grasping—A Deep Reinforcement Learning Approach This paper focuses on developing a robotic object grasping approach that possesses the ability of self-learning, is suitable for small-volume large variety... YL Chen,YR Cai,MY Cheng - 《Machines》 被引量: 0发表: 2023...
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknown distribution. We model the distribution over MDPs using a hierarchical Bayesian infinite mixture model. For each nove...
Reinforcement Learning (RL) algorithms have recently been applied in the RS research, which models the sequential user behaviors as Markov Decision Process (MDP) and utilizes RL to generate recommendations at each decision step [32, 58]. The RL-based recommender system is capable of handling the...
oMultitask Learning / Domain Adaptation oMultitask Kernel Methods oMultitask Deep Learning 工具包 oMulti-Task Learning: Theory, Algorithms, and Applications oAn Tutorial for Regularized Multi-task Learning using the package RMTL oSparseMTL Toolbox ...
oMultitask Deep Learning 工具包 oMulti-Task Learning: Theory, Algorithms, and Applications oAn Tutorial for Regularized Multi-task Learning using the package RMTL oSparseMTL Toolbox oProbabilistic Machine Learning Multilabel学习 •KEEL-dataset repository 凸优化 •EE364a: Convex Optimization I Profe...
Multi-asset closed-loop reservoir management using deep reinforcement learning Closed-loop reservoir management (CLRM), in which history matching and production optimization are performed multiple times over the life of an asset, can ... Y Nasir,LJ Durlofsky - 《Computational Geosciences》 被引量: ...