3 papers
cs.LG2020
Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents
Eric Zelikman, William Yin, Kenneth Wang
A significant challenge in developing AI that can generalize well is designing agents that learn about their world without being told what to learn, and apply that learning to chal…
cs.MA2019
Modeling Sensorimotor Coordination as Multi-Agent Reinforcement Learning with Differentiable Communication
Bowen Jing, William Yin
Multi-agent reinforcement learning has shown promise on a variety of cooperative tasks as a consequence of recent developments in differentiable inter-agent communication. However,…
cs.CL2018
Improving Context-Aware Semantic Relationships in Sparse Mobile Datasets
Peter Hansel, Nik Marda, William Yin
Traditional semantic similarity models often fail to encapsulate the external context in which texts are situated. However, textual datasets generated on mobile platforms can help…