A research team from McGill University, Université de Montréal, DeepMind and Mila presents an end-to-end, model-based deep reinforcement learning (RL) agent that dynamically attends to relevant parts of its environments to facilitate out-of-distribution (OOD) and systematic generalization.

Here is a quick read: Yoshua Bengio Team Designs Consciousness-Inspired Planning Agent for Model-Based RL.

The paper A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning is on arXiv.



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