Obserwuj
Zeyu Zheng
Tytuł
Cytowane przez
Cytowane przez
Rok
Poseidon: An efficient communication architecture for distributed deep learning on {GPU} clusters
H Zhang, Z Zheng, S Xu, W Dai, Q Ho, X Liang, Z Hu, J Wei, P Xie, ...
2017 USENIX Annual Technical Conference (USENIX ATC 17), 181-193, 2017
3842017
On learning intrinsic rewards for policy gradient methods
Z Zheng, J Oh, S Singh
Advances in Neural Information Processing Systems, 4644-4654, 2018
1832018
Parallelizing sequential graph computations
W Fan, J Xu, Y Wu, W Yu, J Jiang, Z Zheng, B Zhang, Y Cao, C Tian
Proceedings of the 2017 ACM International Conference on Management of Data …, 2017
1102017
What Can Learned Intrinsic Rewards Capture?
Z Zheng, J Oh, M Hessel, Z Xu, M Kroiss, H Van Hasselt, D Silver, S Singh
International Conference on Machine Learning, 11436-11446, 2020
782020
Automated multi-layer optical design via deep reinforcement learning
H Wang, Z Zheng, C Ji, LJ Guo
Machine Learning: Science and Technology 2 (2), 025013, 2021
492021
Understanding plasticity in neural networks
C Lyle, Z Zheng, E Nikishin, BA Pires, R Pascanu, W Dabney
arXiv preprint arXiv:2303.01486, 2023
142023
Adaptive Pairwise Weights for Temporal Credit Assignment
Z Zheng, R Vuorio, R Lewis, S Singh
Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 9225-9232, 2022
4*2022
Learning State Representations from Random Deep Action-conditional Predictions
Z Zheng, V Veeriah, R Vuorio, RL Lewis, S Singh
Advances in Neural Information Processing Systems 34, 23679-23691, 2021
42021
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations
N Vadori, L Ardon, S Ganesh, T Spooner, S Amrouni, J Vann, M Xu, ...
arXiv preprint arXiv:2210.07184, 2022
32022
GrASP: Gradient-Based Affordance Selection for Planning
V Veeriah, Z Zheng, R Lewis, S Singh
arXiv preprint arXiv:2202.04772, 2022
32022
Advances in Deep Reinforcement Learning: Intrinsic Rewards, Temporal Credit Assignment, State Representations, and Value-equivalent Models
Z Zheng
2022
Reinforcement learning using meta-learned intrinsic rewards
Z Zheng, J Oh, SS Baveja
US Patent App. 17/033,410, 2021
2021
Supplementary Material: On Learning Intrinsic Rewards for Policy Gradient Methods
Z Zheng, J Oh, S Singh
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