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2020 – today
- 2023
[c8]Pranav Khanna, Guy Tennenholtz, Nadav Merlis, Shie Mannor, Chen Tessler:
Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning. AAMAS 2023: 2430-2432- [i14]Chen Tessler, Yoni Kasten, Yunrong Guo, Shie Mannor, Gal Chechik, Xue Bin Peng:
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters. CoRR abs/2305.02195 (2023) - 2022
- [j2]Chen Tessler, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik, Shie Mannor:
Reinforcement Learning for Datacenter Congestion Control. SIGMETRICS Perform. Evaluation Rev. 49(2): 43-46 (2022) - [c7]Chen Tessler, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik, Shie Mannor:
Reinforcement Learning for Datacenter Congestion Control. AAAI 2022: 12615-12621 - [c6]Yakov Miron, Chana Ross, Yuval Goldfracht, Chen Tessler, Dotan Di Castro:
Towards Autonomous Grading In The Real World. IROS 2022: 11940-11946 - [i13]Yakov Miron, Chana Ross, Yuval Goldfracht, Chen Tessler, Dotan Di Castro:
Towards Autonomous Grading In The Real World. CoRR abs/2206.06091 (2022) - [i12]Benjamin Fuhrer, Yuval Shpigelman, Chen Tessler, Shie Mannor, Gal Chechik, Eitan Zahavi, Gal Dalal:
Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs. CoRR abs/2207.02295 (2022) - 2021
- [j1]Stav Belogolovsky
, Philip Korsunsky, Shie Mannor, Chen Tessler, Tom Zahavy:
Inverse reinforcement learning in contextual MDPs. Mach. Learn. 110(9): 2295-2334 (2021) - [c5]Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir:
Ensemble Bootstrapping for Q-Learning. ICML 2021: 8454-8463 - [i11]Chen Tessler, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik, Shie Mannor:
Reinforcement Learning for Datacenter Congestion Control. CoRR abs/2102.09337 (2021) - [i10]Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir:
Ensemble Bootstrapping for Q-Learning. CoRR abs/2103.00445 (2021) - 2020
- [i9]Chen Tessler, Shie Mannor:
Maximizing the Total Reward via Reward Tweaking. CoRR abs/2002.03327 (2020)
2010 – 2019
- 2019
- [c4]Chen Tessler, Daniel J. Mankowitz, Shie Mannor:
Reward Constrained Policy Optimization. ICLR (Poster) 2019 - [c3]Chen Tessler, Yonathan Efroni, Shie Mannor:
Action Robust Reinforcement Learning and Applications in Continuous Control. ICML 2019: 6215-6224 - [c2]Chen Tessler, Guy Tennenholtz, Shie Mannor:
Distributional Policy Optimization: An Alternative Approach for Continuous Control. NeurIPS 2019: 1350-1360 - [i8]Chen Tessler, Yonathan Efroni, Shie Mannor:
Action Robust Reinforcement Learning and Applications in Continuous Control. CoRR abs/1901.09184 (2019) - [i7]Chen Tessler, Tom Zahavy, Deborah Cohen, Daniel J. Mankowitz, Shie Mannor:
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces. CoRR abs/1905.09700 (2019) - [i6]Philip Korsunsky, Stav Belogolovsky, Tom Zahavy, Chen Tessler, Shie Mannor:
Inverse Reinforcement Learning in Contextual MDPs. CoRR abs/1905.09710 (2019) - [i5]Chen Tessler, Guy Tennenholtz, Shie Mannor:
Distributional Policy Optimization: An Alternative Approach for Continuous Control. CoRR abs/1905.09855 (2019) - [i4]Chen Tessler, Nadav Merlis, Shie Mannor:
Stabilizing Off-Policy Reinforcement Learning with Conservative Policy Gradients. CoRR abs/1910.01062 (2019) - [i3]Erez Schwartz, Guy Tennenholtz, Chen Tessler, Shie Mannor:
Natural Language State Representation for Reinforcement Learning. CoRR abs/1910.02789 (2019) - 2018
- [i2]Chen Tessler, Daniel J. Mankowitz, Shie Mannor:
Reward Constrained Policy Optimization. CoRR abs/1805.11074 (2018) - 2017
- [c1]Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor:
A Deep Hierarchical Approach to Lifelong Learning in Minecraft. AAAI 2017: 1553-1561 - 2016
- [i1]Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor:
A Deep Hierarchical Approach to Lifelong Learning in Minecraft. CoRR abs/1604.07255 (2016)
Coauthor Index
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last updated on 2023-05-24 22:53 CEST by the dblp team
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