Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Lazić, Nevena"'
Autor:
D'Ambrosio, David B., Abeyruwan, Saminda, Graesser, Laura, Iscen, Atil, Amor, Heni Ben, Bewley, Alex, Reed, Barney J., Reymann, Krista, Takayama, Leila, Tassa, Yuval, Choromanski, Krzysztof, Coumans, Erwin, Jain, Deepali, Jaitly, Navdeep, Jaques, Natasha, Kataoka, Satoshi, Kuang, Yuheng, Lazic, Nevena, Mahjourian, Reza, Moore, Sherry, Oslund, Kenneth, Shankar, Anish, Sindhwani, Vikas, Vanhoucke, Vincent, Vesom, Grace, Xu, Peng, Sanketi, Pannag R.
Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in c
Externí odkaz:
http://arxiv.org/abs/2408.03906
Autor:
D'Ambrosio, David B., Abelian, Jonathan, Abeyruwan, Saminda, Ahn, Michael, Bewley, Alex, Boyd, Justin, Choromanski, Krzysztof, Cortes, Omar, Coumans, Erwin, Ding, Tianli, Gao, Wenbo, Graesser, Laura, Iscen, Atil, Jaitly, Navdeep, Jain, Deepali, Kangaspunta, Juhana, Kataoka, Satoshi, Kouretas, Gus, Kuang, Yuheng, Lazic, Nevena, Lynch, Corey, Mahjourian, Reza, Moore, Sherry Q., Nguyen, Thinh, Oslund, Ken, Reed, Barney J, Reymann, Krista, Sanketi, Pannag R., Shankar, Anish, Sermanet, Pierre, Sindhwani, Vikas, Singh, Avi, Vanhoucke, Vincent, Vesom, Grace, Xu, Peng
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts to
Externí odkaz:
http://arxiv.org/abs/2309.03315
Autor:
Tracey, Brendan D., Michi, Andrea, Chervonyi, Yuri, Davies, Ian, Paduraru, Cosmin, Lazic, Nevena, Felici, Federico, Ewalds, Timo, Donner, Craig, Galperti, Cristian, Buchli, Jonas, Neunert, Michael, Huber, Andrea, Evens, Jonathan, Kurylowicz, Paula, Mankowitz, Daniel J., Riedmiller, Martin, Team, The TCV
Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbacks compared to traditional feedback control approaches for magnetic co
Externí odkaz:
http://arxiv.org/abs/2307.11546
Autor:
Yin, Dong, Thiagarajan, Sridhar, Lazic, Nevena, Rajaraman, Nived, Hao, Botao, Szepesvari, Csaba
The focus of this work is sample-efficient deep reinforcement learning (RL) with a simulator. One useful property of simulators is that it is typically easy to reset the environment to a previously observed state. We propose an algorithmic framework,
Externí odkaz:
http://arxiv.org/abs/2301.12579
We study the non-stationary stochastic multi-armed bandit problem, where the reward statistics of each arm may change several times during the course of learning. The performance of a learning algorithm is evaluated in terms of their dynamic regret,
Externí odkaz:
http://arxiv.org/abs/2201.06532
We study query and computationally efficient planning algorithms with linear function approximation and a simulator. We assume that the agent only has local access to the simulator, meaning that the agent can only query the simulator at states that h
Externí odkaz:
http://arxiv.org/abs/2108.05533
In this work, we study algorithms for learning in infinite-horizon undiscounted Markov decision processes (MDPs) with function approximation. We first show that the regret analysis of the Politex algorithm (a version of regularized policy iteration)
Externí odkaz:
http://arxiv.org/abs/2102.12611
Many reinforcement learning algorithms can be seen as versions of approximate policy iteration (API). While standard API often performs poorly, it has been shown that learning can be stabilized by regularizing each policy update by the KL-divergence
Externí odkaz:
http://arxiv.org/abs/2102.06234
Autor:
Tracey, Brendan D., Michi, Andrea, Chervonyi, Yuri, Davies, Ian, Paduraru, Cosmin, Lazic, Nevena, Felici, Federico, Ewalds, Timo, Donner, Craig, Galperti, Cristian, Buchli, Jonas, Neunert, Michael, Huber, Andrea, Evens, Jonathan, Kurylowicz, Paula, Mankowitz, Daniel J., Riedmiller, Martin
Publikováno v:
In Fusion Engineering and Design March 2024 200
Autor:
Mao, Hongzi, Gu, Chenjie, Wang, Miaosen, Chen, Angie, Lazic, Nevena, Levine, Nir, Pang, Derek, Claus, Rene, Hechtman, Marisabel, Chiang, Ching-Han, Chen, Cheng, Han, Jingning
In modern video encoders, rate control is a critical component and has been heavily engineered. It decides how many bits to spend to encode each frame, in order to optimize the rate-distortion trade-off over all video frames. This is a challenging co
Externí odkaz:
http://arxiv.org/abs/2012.05339