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pro vyhledávání: '"da Silva, Bruno"'
Novel reinforcement learning algorithms, or improvements on existing ones, are commonly justified by evaluating their performance on benchmark environments and are compared to an ever-changing set of standard algorithms. However, despite numerous cal
Externí odkaz:
http://arxiv.org/abs/2406.16241
Autor:
Chaudhari, Shreyas, Aggarwal, Pranjal, Murahari, Vishvak, Rajpurohit, Tanmay, Kalyan, Ashwin, Narasimhan, Karthik, Deshpande, Ameet, da Silva, Bruno Castro
State-of-the-art large language models (LLMs) have become indispensable tools for various tasks. However, training LLMs to serve as effective assistants for humans requires careful consideration. A promising approach is reinforcement learning from hu
Externí odkaz:
http://arxiv.org/abs/2404.08555
Autor:
Gupta, Dhawal, Jordan, Scott M., Chaudhari, Shreyas, Liu, Bo, Thomas, Philip S., da Silva, Bruno Castro
In this paper, we introduce a fresh perspective on the challenges of credit assignment and policy evaluation. First, we delve into the nuances of eligibility traces and explore instances where their updates may result in unexpected credit assignment
Externí odkaz:
http://arxiv.org/abs/2312.12972
Designing reward functions for efficiently guiding reinforcement learning (RL) agents toward specific behaviors is a complex task. This is challenging since it requires the identification of reward structures that are not sparse and that avoid inadve
Externí odkaz:
http://arxiv.org/abs/2310.19007
Autor:
Melotti, Gledson, Bastos, Johann J. S., da Silva, Bruno L. S., Zanotelli, Tiago, Premebida, Cristiano
Object recognition is a crucial step in perception systems for autonomous and intelligent vehicles, as evidenced by the numerous research works in the topic. In this paper, object recognition is explored by using multisensory and multimodality approa
Externí odkaz:
http://arxiv.org/abs/2310.05951
Autor:
Kostas, James E., Jordan, Scott M., Chandak, Yash, Theocharous, Georgios, Gupta, Dhawal, White, Martha, da Silva, Bruno Castro, Thomas, Philip S.
Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic neural networks. The coagent framework offers an alternative to backpr
Externí odkaz:
http://arxiv.org/abs/2305.09838
Autor:
Chandak, Yash, Shankar, Shiv, Bastian, Nathaniel D., da Silva, Bruno Castro, Brunskil, Emma, Thomas, Philip S.
Methods for sequential decision-making are often built upon a foundational assumption that the underlying decision process is stationary. This limits the application of such methods because real-world problems are often subject to changes due to exte
Externí odkaz:
http://arxiv.org/abs/2301.10330
Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision problems where agents may have different preferences over (possibly conflicting) reward functions. Such algorithms often learn a set of policies (each optimized for a
Externí odkaz:
http://arxiv.org/abs/2301.07784
Autor:
da Silva, Bruno C, Momtaz, Zahra S, Bruas, Lucas, Rouviére, Jean-Luc, Okuno, Hanako, Cooper, David, Den-Hertog, Martien I
Publikováno v:
Applied Physics Letters, American Institute of Physics, 2022, 121 (12), pp.123503
Momentum resolved 4D-STEM, also called center of mass (CoM) analysis, has been used to measure the long range built-in electric field of a silicon p-n junction. The effect of different STEM modes and the trade-off between spatial resolution and elect
Externí odkaz:
http://arxiv.org/abs/2211.00971
Autor:
da Silva, Bruno C., Momtaz, Zahra S., Monroy, Eva, Okuno, Hanako, Rouviere, Jean-Luc, Cooper, David, den-Hertog, Martien I.
A key issue in the development of high-performance semiconductor devices is the ability to properly measure active dopants at the nanometer scale. 4D scanning transmission electron microscopy and off-axis electron holography have opened up the possib
Externí odkaz:
http://arxiv.org/abs/2209.09633