Natasha Jaques
Assistant Professor, University of Washington | Research Scientist, Google DeepMind | Multi-Agent RL Pioneer
Assistant Professor of Computer Science & Engineering at the University of Washington, Senior Research Scientist at Google DeepMind, and PhD from MIT Media Lab. Pioneer in Social Reinforcement Learning, Multi-Agent Coordination, and Open-Ended Learning. Known for developing intrinsic social motivation mechanisms, PAIRED (adversarial environment generation for robust agent generalization), and evaluating cooperative multi-agent dynamics.
Areas of focus
Professional niches
Proof of Work
PAIRED: Emergent Complexity and Zero-shot Transfer via Adversarial Environment Generation
Invented Protagonist Antagonist Induced Regret Environment Design (PAIRED), using an adversarial teacher agent to procedurally generate training curriculums that optimize agent regret and zero-shot out-of-distribution transfer.
Adversarial curriculum generators can encounter unstable training dynamics when reward estimation variances are high.
Context: NeurIPS 2020 spotlight, adversarial RL, procedural generation.
View missionEmergent Social Learning via Multi-Agent Reinforcement Learning
Demonstrated that multi-agent reinforcement learning with intrinsic social curiosity incentives enables agents to spontaneously acquire social learning, imitation, and cultural transmission without human expert demonstrations.
Social imitation policies can converge to suboptimal collective traps if demonstrator agents experience local reward plateaus.
Context: Deep RL, multi-agent gridworld environments, social reward functions.
View mission