no code implementations • 2 May 2023 • Chen Tessler, Yoni Kasten, Yunrong Guo, Shie Mannor, Gal Chechik, Xue Bin Peng
In this work, we present Conditional Adversarial Latent Models (CALM), an approach for generating diverse and directable behaviors for user-controlled interactive virtual characters.
1 code implementation • 5 Jul 2022 • Benjamin Fuhrer, Yuval Shpigelman, Chen Tessler, Shie Mannor, Gal Chechik, Eitan Zahavi, Gal Dalal
As communication protocols evolve, datacenter network utilization increases.
no code implementations • 13 Jun 2022 • Yakov Miron, Chana Ross, Yuval Goldfracht, Chen Tessler, Dotan Di Castro
As the heuristics are capable of successfully solving the task in the simulated environment, we show they can be leveraged to guide a learning agent which can generalize and solve the task both in simulation and in a scaled prototype environment.
no code implementations • 28 Feb 2021 • Oren Peer, Chen Tessler, Nadav Merlis, Ron Meir
Finally, We demonstrate the superior performance of a deep RL variant of EBQL over other deep QL algorithms for a suite of ATARI games.
no code implementations • 18 Feb 2021 • Chen Tessler, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik, Shie Mannor
We approach the task of network congestion control in datacenters using Reinforcement Learning (RL).
no code implementations • 9 Feb 2020 • Chen Tessler, Shie Mannor
In reinforcement learning, the discount factor $\gamma$ controls the agent's effective planning horizon.
no code implementations • 2 Oct 2019 • Erez Schwartz, Guy Tennenholtz, Chen Tessler, Shie Mannor
Recent advances in reinforcement learning have shown its potential to tackle complex real-life tasks.
no code implementations • 2 Oct 2019 • Pranav Khanna, Guy Tennenholtz, Nadav Merlis, Shie Mannor, Chen Tessler
In recent years, there has been significant progress in applying deep reinforcement learning (RL) for solving challenging problems across a wide variety of domains.
no code implementations • 25 Sep 2019 • Philip Korsunsky, Stav Belogolovsky, Tom Zahavy, Chen Tessler, Shie Mannor
In this setting, the reward, which is unknown to the agent, is a function of a static parameter referred to as the context.
no code implementations • 25 Sep 2019 • Chen Tessler, Nadav Merlis, Shie Mannor
In recent years, advances in deep learning have enabled the application of reinforcement learning algorithms in complex domains.
no code implementations • 23 May 2019 • Chen Tessler, Tom Zahavy, Deborah Cohen, Daniel J. Mankowitz, Shie Mannor
We propose a computationally efficient algorithm that combines compressed sensing with imitation learning to solve text-based games with combinatorial action spaces.
3 code implementations • NeurIPS 2019 • Chen Tessler, Guy Tennenholtz, Shie Mannor
We show that optimizing over such sets results in local movement in the action space and thus convergence to sub-optimal solutions.
2 code implementations • 23 May 2019 • Stav Belogolovsky, Philip Korsunsky, Shie Mannor, Chen Tessler, Tom Zahavy
Most importantly, we show both theoretically and empirically that our algorithms perform zero-shot transfer (generalize to new and unseen contexts).
2 code implementations • 26 Jan 2019 • Chen Tessler, Yonathan Efroni, Shie Mannor
In this work we formalize two new criteria of robustness to action uncertainty.
1 code implementation • ICLR 2019 • Chen Tessler, Daniel J. Mankowitz, Shie Mannor
Solving tasks in Reinforcement Learning is no easy feat.
no code implementations • 25 Apr 2016 • Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J. Mankowitz, Shie Mannor
Skill distillation enables the HDRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network.