Zobrazeno 1 - 10
of 3 384
pro vyhledávání: '"Towers, P"'
Rainbow Deep Q-Network (DQN) demonstrated combining multiple independent enhancements could significantly boost a reinforcement learning (RL) agent's performance. In this paper, we present "Beyond The Rainbow" (BTR), a novel algorithm that integrates
Externí odkaz:
http://arxiv.org/abs/2411.03820
Autor:
Towers, Sebastian, Kalisz, Aleksandra, Robert, Philippe A., Higueruelo, Alicia, Vianello, Francesca, Tsai, Ming-Han Chloe, Steel, Harrison, Foerster, Jakob N.
Anti-viral therapies are typically designed to target only the current strains of a virus. Game theoretically, this corresponds to a short-sighted, or myopic, response. However, therapy-induced selective pressures act on viruses to drive the emergenc
Externí odkaz:
http://arxiv.org/abs/2409.10588
Publikováno v:
ECAI 2024
Future reward estimation is a core component of reinforcement learning agents; i.e., Q-value and state-value functions, predicting an agent's sum of future rewards. Their scalar output, however, obfuscates when or what individual future rewards an ag
Externí odkaz:
http://arxiv.org/abs/2408.08230
Autor:
Towers, Mark, Kwiatkowski, Ariel, Terry, Jordan, Balis, John U., De Cola, Gianluca, Deleu, Tristan, Goulão, Manuel, Kallinteris, Andreas, Krimmel, Markus, KG, Arjun, Perez-Vicente, Rodrigo, Pierré, Andrea, Schulhoff, Sander, Tai, Jun Jet, Tan, Hannah, Younis, Omar G.
Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence. However, despite its promise, RL research is often hindered by the lack of standardization in environment and a
Externí odkaz:
http://arxiv.org/abs/2407.17032
Autor:
Ouaridi, A. Fernandez, Towers, D. A.
A characterization of the finite-dimensional Leibniz algebras with an abelian subalgebra of codimension two over a field $\mathbb{F}$ of characteristic $p\neq2$ is given. In short, a finite-dimensional Leibniz algebra of dimension $n$ with an abelian
Externí odkaz:
http://arxiv.org/abs/2407.11757
This paper studies the abelian subalgebras and ideals of maximal dimension of Poisson algebras $\mathcal{P}$ of dimension $n$. We introduce the invariants $\alpha$ and $\beta$ for Poisson algebras, which correspond to the dimension of an abelian suba
Externí odkaz:
http://arxiv.org/abs/2405.05859
The boundless possibility of neural networks which can be used to solve a problem -- each with different performance -- leads to a situation where a Deep Learning expert is required to identify the best neural network. This goes against the hope of r
Externí odkaz:
http://arxiv.org/abs/2404.02189
Autor:
Towers, David A.
This paper is concerned with generalising the results for Lie $CT$-algebras to Leibniz algebras. In some cases our results give a generalisation even for the case of a Lie algebra. Results on $A$-algebras are used to show every Leibniz $CT$-algebra o
Externí odkaz:
http://arxiv.org/abs/2402.17331
Policy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a unifying perspective that encompasses numerous algorithms. These algorithms are derived through the selection of a mirror map and enjoy finite-time convergence
Externí odkaz:
http://arxiv.org/abs/2402.05187
Autor:
Huang, Shengyi, Gallouédec, Quentin, Felten, Florian, Raffin, Antonin, Dossa, Rousslan Fernand Julien, Zhao, Yanxiao, Sullivan, Ryan, Makoviychuk, Viktor, Makoviichuk, Denys, Danesh, Mohamad H., Roumégous, Cyril, Weng, Jiayi, Chen, Chufan, Rahman, Md Masudur, Araújo, João G. M., Quan, Guorui, Tan, Daniel, Klein, Timo, Charakorn, Rujikorn, Towers, Mark, Berthelot, Yann, Mehta, Kinal, Chakraborty, Dipam, KG, Arjun, Charraut, Valentin, Ye, Chang, Liu, Zichen, Alegre, Lucas N., Nikulin, Alexander, Hu, Xiao, Liu, Tianlin, Choi, Jongwook, Yi, Brent
In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curves are rarely available. As a result, it is usually necessary to repro
Externí odkaz:
http://arxiv.org/abs/2402.03046