Zobrazeno 1 - 10
of 4 522
pro vyhledávání: '"COURVILLE, A."'
In the coming years, AI agents will be used for making more complex decisions, including in situations involving many different groups of people. One big challenge is that AI agent tends to act in its own interest, unlike humans who often think about
Externí odkaz:
http://arxiv.org/abs/2409.02960
Autor:
Nguyen, Bac, Uhlich, Stefan, Cardinaux, Fabien, Mauch, Lukas, Edraki, Marzieh, Courville, Aaron
Handling distribution shifts from training data, known as out-of-distribution (OOD) generalization, poses a significant challenge in the field of machine learning. While a pre-trained vision-language model like CLIP has demonstrated remarkable zero-s
Externí odkaz:
http://arxiv.org/abs/2407.03036
Deep reinforcement learning (deep RL) has achieved tremendous success on various domains through a combination of algorithmic design and careful selection of hyper-parameters. Algorithmic improvements are often the result of iterative enhancements bu
Externí odkaz:
http://arxiv.org/abs/2406.17523
Learning generalist embodied agents, able to solve multitudes of tasks in different domains is a long-standing problem. Reinforcement learning (RL) is hard to scale up as it requires a complex reward design for each task. In contrast, language can sp
Externí odkaz:
http://arxiv.org/abs/2406.18043
The growing presence of artificially intelligent agents in everyday decision-making, from LLM assistants to autonomous vehicles, hints at a future in which conflicts may arise from each agent optimizing individual interests. In general-sum games thes
Externí odkaz:
http://arxiv.org/abs/2406.14662
We uncover a surprising phenomenon in deep reinforcement learning: training a diverse ensemble of data-sharing agents -- a well-established exploration strategy -- can significantly impair the performance of the individual ensemble members when compa
Externí odkaz:
http://arxiv.org/abs/2405.04342
In various real-world scenarios, interactions among agents often resemble the dynamics of general-sum games, where each agent strives to optimize its own utility. Despite the ubiquitous relevance of such settings, decentralized machine learning algor
Externí odkaz:
http://arxiv.org/abs/2405.01035
Autor:
Lavoie, Samuel, Kirichenko, Polina, Ibrahim, Mark, Assran, Mahmoud, Wilson, Andrew Gordon, Courville, Aaron, Ballas, Nicolas
There are a thousand ways to caption an image. Contrastive Language Pretraining (CLIP) on the other hand, works by mapping an image and its caption to a single vector -- limiting how well CLIP-like models can represent the diverse ways to describe an
Externí odkaz:
http://arxiv.org/abs/2405.00740
Selective attention helps us focus on task-relevant aspects in the constant flood of our sensory input. This constraint in our perception allows us to robustly generalize under distractions and to new compositions of perceivable concepts. Transformer
Externí odkaz:
http://arxiv.org/abs/2404.15721
We investigate the challenge of multi-agent deep reinforcement learning in partially competitive environments, where traditional methods struggle to foster reciprocity-based cooperation. LOLA and POLA agents learn reciprocity-based cooperative polici
Externí odkaz:
http://arxiv.org/abs/2404.06519