Zobrazeno 1 - 10
of 146
pro vyhledávání: '"GEORGESCU, Raluca"'
The performance of embodied agents has been shown to improve by increasing model parameters, dataset size, and compute. This has been demonstrated in domains from robotics to video games, when generative learning objectives on offline datasets (pre-t
Externí odkaz:
http://arxiv.org/abs/2411.04434
Autor:
Schäfer, Lukas, Jones, Logan, Kanervisto, Anssi, Cao, Yuhan, Rashid, Tabish, Georgescu, Raluca, Bignell, Dave, Sen, Siddhartha, Gavito, Andrea Treviño, Devlin, Sam
Video games have served as useful benchmarks for the decision making community, but going beyond Atari games towards training agents in modern games has been prohibitively expensive for the vast majority of the research community. Recent progress in
Externí odkaz:
http://arxiv.org/abs/2312.02312
Autor:
Milani, Stephanie, Juliani, Arthur, Momennejad, Ida, Georgescu, Raluca, Rzpecki, Jaroslaw, Shaw, Alison, Costello, Gavin, Fang, Fei, Devlin, Sam, Hofmann, Katja
We aim to understand how people assess human likeness in navigation produced by people and artificially intelligent (AI) agents in a video game. To this end, we propose a novel AI agent with the goal of generating more human-like behavior. We collect
Externí odkaz:
http://arxiv.org/abs/2303.02160
Autor:
Pearce, Tim, Rashid, Tabish, Kanervisto, Anssi, Bignell, Dave, Sun, Mingfei, Georgescu, Raluca, Macua, Sergio Valcarcel, Tan, Shan Zheng, Momennejad, Ida, Hofmann, Katja, Devlin, Sam
Publikováno v:
ICLR 2023
Diffusion models have emerged as powerful generative models in the text-to-image domain. This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments. Human behaviour is stochastic and
Externí odkaz:
http://arxiv.org/abs/2301.10677
Autor:
Carroll, Micah, Paradise, Orr, Lin, Jessy, Georgescu, Raluca, Sun, Mingfei, Bignell, David, Milani, Stephanie, Hofmann, Katja, Hausknecht, Matthew, Dragan, Anca, Devlin, Sam
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same idea also applies naturally to sequential decision-making, where many
Externí odkaz:
http://arxiv.org/abs/2211.10869
Modern AAA video games feature huge game levels and maps which are increasingly hard for level testers to cover exhaustively. As a result, games often ship with catastrophic bugs such as the player falling through the floor or being stuck in walls. W
Externí odkaz:
http://arxiv.org/abs/2209.00570
Autor:
Anichitoae, Florina Magdalena a, b, Dobrean, Anca a, c, ⁎, Georgescu, Raluca Diana c, Roman, Gabriela Diana d
Publikováno v:
In Aggression and Violent Behavior January 2025 80
Autor:
Carroll, Micah, Lin, Jessy, Paradise, Orr, Georgescu, Raluca, Sun, Mingfei, Bignell, David, Milani, Stephanie, Hofmann, Katja, Hausknecht, Matthew, Dragan, Anca, Devlin, Sam
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same idea also applies naturally to sequential decision making, where many
Externí odkaz:
http://arxiv.org/abs/2204.13326
Autor:
GEORGESCU, Raluca Iuliana1 raluca.georgescu@infinitumgroup.com, BODISLAV, Dumitru Alexandru2 alex.bodislav@ase.ro
Publikováno v:
Theoretical & Applied Economics. Autumn2024, Vol. 31 Issue 3, p167-176. 10p.
Autor:
Devlin, Sam, Georgescu, Raluca, Momennejad, Ida, Rzepecki, Jaroslaw, Zuniga, Evelyn, Costello, Gavin, Leroy, Guy, Shaw, Ali, Hofmann, Katja
Publikováno v:
Proceedings of the 38th International Conference on Machine Learning (ICML), 139:2644-2653, 2021
A key challenge on the path to developing agents that learn complex human-like behavior is the need to quickly and accurately quantify human-likeness. While human assessments of such behavior can be highly accurate, speed and scalability are limited.
Externí odkaz:
http://arxiv.org/abs/2105.09637