2.3k citations · 3k across the 20 of their papers we have counts for
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Model-based Adversarial Meta-Reinforcement Learning
Zichuan Lin, Garrett Thomas, Guangwen Yang +1
Meta-reinforcement learning (meta-RL) aims to learn from multiple training tasks the ability to adapt efficiently to unseen test tasks. Despite the success, existing meta-RL algori…
Distributional Reward Decomposition for Reinforcement Learning
Zichuan Lin, Li Zhao, Derek Yang +3
Many reinforcement learning (RL) tasks have specific properties that can be leveraged to modify existing RL algorithms to adapt to those tasks and further improve performance, and…
An Adaptive Remote Stochastic Gradient Method for Training Neural Networks
Yushu Chen, Hao Jing, Wenlai Zhao +5
We present the remote stochastic gradient (RSG) method, which computes the gradients at configurable remote observation points, in order to improve the convergence rate and suppres…
swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor
Mingzhen Li, Changxi Liu, Jianjin Liao +8
The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to pro…
Episodic Memory Deep Q-Networks
Zichuan Lin, Tianqi Zhao, Guangwen Yang +1
Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are kn…
Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks
Hao Zhang, Shizhen Xu, Graham Neubig +4
Recent deep learning (DL) models have moved beyond static network architectures to dynamic ones, handling data where the network structure changes every example, such as sequences…