Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Tamar, Aviv"'
Autor:
Mutti, Mirco, Tamar, Aviv
Meta reinforcement learning sets a distribution over a set of tasks on which the agent can train at will, then is asked to learn an optimal policy for any test task efficiently. In this paper, we consider a finite set of tasks modeled through Markov
Externí odkaz:
http://arxiv.org/abs/2406.02282
Autor:
Greshler, Nir, Eli, David Ben, Rabinovitz, Carmel, Guetta, Gabi, Gispan, Liran, Zohar, Guy, Tamar, Aviv
The combination of Monte Carlo tree search and neural networks has revolutionized online planning. As neural network approximations are often imperfect, we ask whether uncertainty estimates about the network outputs could be used to improve planning.
Externí odkaz:
http://arxiv.org/abs/2406.02103
We present RoboArm-NMP, a learning and evaluation environment that allows simple and thorough evaluations of Neural Motion Planning (NMP) algorithms, focused on robotic manipulators. Our Python-based environment provides baseline implementations for
Externí odkaz:
http://arxiv.org/abs/2405.16335
Manipulating objects is a hallmark of human intelligence, and an important task in domains such as robotics. In principle, Reinforcement Learning (RL) offers a general approach to learn object manipulation. In practice, however, domains with more tha
Externí odkaz:
http://arxiv.org/abs/2404.01220
Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration. Existing meta-RL algorithms are characterized by low sample efficiency, and mostly focus on low-dimensional task distribu
Externí odkaz:
http://arxiv.org/abs/2403.09859
Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions. While approaches such as Bayesian inference are well-studied frameworks for adapting models to evidence, we bu
Externí odkaz:
http://arxiv.org/abs/2310.12862
Publikováno v:
ICML 2023
Learning from rewards (i.e., reinforcement learning or RL) and learning to imitate a teacher (i.e., teacher-student learning) are two established approaches for solving sequential decision-making problems. To combine the benefits of these different f
Externí odkaz:
http://arxiv.org/abs/2307.03186
Autor:
Daniel, Tal, Tamar, Aviv
We propose a new object-centric video prediction algorithm based on the deep latent particle (DLP) representation. In comparison to existing slot- or patch-based representations, DLPs model the scene using a set of keypoints with learned parameters f
Externí odkaz:
http://arxiv.org/abs/2306.05957
We study zero-shot generalization in reinforcement learning-optimizing a policy on a set of training tasks to perform well on a similar but unseen test task. To mitigate overfitting, previous work explored different notions of invariance to the task.
Externí odkaz:
http://arxiv.org/abs/2306.03072
Autor:
Choshen, Era, Tamar, Aviv
In meta reinforcement learning (meta RL), an agent seeks a Bayes-optimal policy -- the optimal policy when facing an unknown task that is sampled from some known task distribution. Previous approaches tackled this problem by inferring a belief over t
Externí odkaz:
http://arxiv.org/abs/2306.02418