Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents

no code implementations • 20 Apr 2020 • 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 challenges with sparse rewards.

Decision Making General Reinforcement Learning +2

Modeling Sensorimotor Coordination as Multi-Agent Reinforcement Learning with Differentiable Communication

no code implementations • 12 Sep 2019 • 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.

Multi-agent Reinforcement Learning reinforcement-learning +1

Improving Context-Aware Semantic Relationships in Sparse Mobile Datasets

no code implementations • 23 Dec 2018 • Peter Hansel, Nik Marda, William Yin

Traditional semantic similarity models often fail to encapsulate the external context in which texts are situated.

Semantic Similarity Semantic Textual Similarity +2