Deep learning, reinforcement learning, and world models
The University of Tokyo · Meta (Israel) · +10 more institutions
Abstract
Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and with neuroscientific findings. In this review, we summarize talks and discussions in the "Deep Learning and Reinforcement Learning" session of the symposium, International Symposium on Artificial Intelligence and Brain Science. In this session, we discussed whether we can achieve comprehensive understanding of human intelligence based on the recent advances of deep learning and reinforcement learning algorithms. Speakers contributed to provide talks about their recent studies…
Citation impact
- FWCI
- 59.24
- Percentile
- 100%
- References
- 96
Authors
8- YMYutaka Matsuo
The University of Tokyo
- YLYann LeCun
Meta (Israel), Courant Institute of Mathematical Sciences, Meta (United States), New York University
- MSManeesh Sahani
Oxford Centre for Computational Neuroscience, University College London
- DPDoina Precup
Google DeepMind (United Kingdom), McGill University
- DSDavid Silver
Google DeepMind (United Kingdom)
Topics & keywords
- Reinforcement learning
- Artificial intelligence
- Computer science
- Session (web analytics)
- Human intelligence
- Deep learning
- Cognitive science
- Reinforcement
- Quality Education