之前同时nips2020,滴滴有一篇文章Multi-Task Deep Reinforcement Learning with Knowledge Transfer for Continuous Control,链接如下: 用的就是知识蒸馏和迁移学习的方法,大致的思路就是第一种,为每一个task 单独训练一个policy network作为教师网络,然后使用这些teacher去同一个student网络,最后使用这一个student去解决Mult...
这种方法通过将原始问题简化为一系列单目标问题,然后使用增强拉格朗日(Augmented Lagrangian)方法来解决这些简化问题,从而有效地处理约束并找到最优解。 在多任务深度学习(Multi-Task Deep Learning)中,也可以将多目标优化框架纳入训练过程中,以找到在多个目标之间取得平衡的模型。这种方法可以同时优化多个标准,并有助于训练...
However, most research so far has focused on the easier case where knowledge transfer is not harmful, i.e., where knowledge from one task cannot negatively impact the performance on another task. In contrast, we present an approach to multi-task deep reinforcement learning based on attention ...
As a step towards developing zero-shot task generalization capabilities in reinforcement learning (RL), we introduce a new RL problem where the agent should learn to execute sequences of instructions after learning useful skills that solve subtasks. In this problem, we consider two types of genera...
In object localization, methods based on a top-down search strategy that focus on learning a policy have been widely researched. The performance of these methods relies heavily on the policy in question. This study proposes a deep Q-network that employs a multi-task learning method to localize...
In recent years, model-free methods that use deep learning have achieved great success in many different reinforcement learning environments. Most successful approaches focus on solving a single task, while multi-task reinforcement learning remains an open problem. In this paper, we present a model ...
为了首先完成多任务学习,我们设计了一种名为“Actor-Mimic”的方法,该方法利用模型压缩技术,使用来自一组游戏专家网络的指导来训练单个多任务网络。特定形式的指导可以有所不同,并且根据经验探索和测试了几种不同的方法。为了实现转移学习,我们将多任务网络视为DQN,它是在一组源任务上预先训练的。我们通过实验证明,这...
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...
Allen School of Computer Science & Engineering at the University of Washington 讲座题目:Passive and Active Multi-Task Representation Learning 讲座摘要:Representation learning has been widely used in many applications. In this talk, I will present our work which uncovers when and why representation ...
多智能体深度强化学习(Multi-Agent Deep Reinforcement Learning, MADRL)是强化学习(Reinforcement Learning, RL)和深度学习(Deep Learning, DL)的交叉领域,其中涉及多个智能体(agent)同时在环境中学习和交互。它尝试解决多智能体系统中的协调、竞争、通信等问题。与单智能体强化学习不同,多智能体系统中的智能体可能有...