Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Mabsout, Bassel"'
In contemporary machine learning workloads, numerous hyper-parameter search algorithms are frequently utilized to efficiently discover high-performing hyper-parameter values, such as learning and regularization rates. As a result, a range of paramete
Externí odkaz:
http://arxiv.org/abs/2310.14671
This study presents "anchor critics", a novel strategy for enhancing the robustness of reinforcement learning (RL) agents in crossing the sim-to-real gap. While RL agents can be successfully trained in simulation, they often encounter difficulties su
Externí odkaz:
http://arxiv.org/abs/2301.06987
Actors and critics in actor-critic reinforcement learning algorithms are functionally separate, yet they often use the same network architectures. This case study explores the performance impact of network sizes when considering actor and critic arch
Externí odkaz:
http://arxiv.org/abs/2102.11893
Publikováno v:
ACM Transactions on Cyber-Physical Systems, Volume 5, Issue 4, October 2021
We focus on the problem of reliably training Reinforcement Learning (RL) models (agents) for stable low-level control in embedded systems and test our methods on a high-performance, custom-built quadrotor platform. A common but often under-studied pr
Externí odkaz:
http://arxiv.org/abs/2012.06656
A critical problem with the practical utility of controllers trained with deep Reinforcement Learning (RL) is the notable lack of smoothness in the actions learned by the RL policies. This trend often presents itself in the form of control signal osc
Externí odkaz:
http://arxiv.org/abs/2012.06644
Autor:
El Mabsout, Bassel
We address the prevalent challenge of Algebraic Data Type duplication in compiler implementations, which results in increased effort, diminished functionality, and complications in synchronizing language constructs across the compiler. To investigate
Externí odkaz:
https://hdl.handle.net/2144/49330
Reinforcement Learning (RL) agents trained in simulated environments and then deployed in the real world are often sensitive to the differences in dynamics presented, commonly termed the sim-to-real gap. With the goal of minimizing this gap on resour
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be968410f796e5be1535f9d5deef5495