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Multi-agent Social Learning

Project Goals: The goal of the project is to develop an algorithm for iteratively constructing recursive hierarchies of options. The hypothesis is that such a method could have the potential to achieve an exponential improvement over flat reinforcement learning policies in learning efficiency by exploring with high...

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Interactive Learning from Vision and Touch

The virtuoso plays the piano with passion, poetry and extraordinary technical ability. As Liszt said (a virtuoso) “must call up scent and blossom, and breathe the breath of life. ” Despite recent advances, how to enable a robot to accurately, naturally and poetically play the piano remains an open and largely unexplored question....

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Self-supervised Learning for Generic Visual Representation

The objective of this proposed collaboration is to explore self-supervised learning beyond the current paradigm of exploiting instance discrimination and contrastive learning, as current exploitation-driven research may be spiraling around a local optimum, while larger-scale algorithmic changes are needed.

Researchers

Tete Xiao, UC Berkeley, http://tetexiao.com/

Piotr Dollár, Facebook AI Research,...

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Training Sparse High Capacity Models with Implicit Neural Networks and Frank-Wolfe

Reducing parameter footprint and inference latency of machine learning models is being driven by diverse applications like mobile vision and on-device intelligence [Choudary 20], and it is increasingly important, as models become increasingly large. In this work, we propose to develop an alternative to the current train/compress paradigm, and instead we will train sparse high-capacity models from scratch, simultaneously achieving low training cost and high sparsity.

Researchers Geoffrey Négiar, UC Berkeley Michael Mahoney, UC Berkeley...

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Using Deep Reinforcement Learning to Generalize Search in Games

Search methods have been instrumental in computing superhuman strategies for large-scale games [1,2,3]. However, existing search techniques are tabular and can therefore have trouble searching far into the future. This is particularly a problem in games with high stochasticity and/or imperfect information. For example, existing search techniques in Hanabi, which is considered an interesting research problem by the AI community [4], are only able to search one move ahead. Even searching two moves ahead is considered intractable for existing techniques. Since real-world situations are...