44 citations · 144 across the 12 of their papers we have counts for
6 papers · 1 filter
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist
Alexander Trott, Sunil Srinivasa, Douwe van der Wal +2
Optimizing economic and public policy is critical to address socioeconomic issues and trade-offs, e.g., improving equality, productivity, or wellness, and poses a complex mechanism…
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning
Stephan Zheng, Alexander Trott, Sunil Srinivasa +2
AI and reinforcement learning (RL) have improved many areas, but are not yet widely adopted in economic policy design, mechanism design, or economics at large. At the same time, cu…
Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents
Eric Zhao, Alexander R. Trott, Caiming Xiong +1
We study the problem of training a principal in a multi-agent general-sum game using reinforcement learning (RL). Learning a robust principal policy requires anticipating the worst…
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Víctor Campos, Alexander Trott, Caiming Xiong +3
Acquiring abilities in the absence of a task-oriented reward function is at the frontier of reinforcement learning research. This problem has been studied through the lens of empow…
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning
Wenling Shang, Alex Trott, Stephan Zheng +2
In many real-world scenarios, an autonomous agent often encounters various tasks within a single complex environment. We propose to build a graph abstraction over the environment s…
Competitive Experience Replay
Hao Liu, Alexander Trott, Richard Socher +1
Deep learning has achieved remarkable successes in solving challenging reinforcement learning (RL) problems when dense reward function is provided. However, in sparse reward enviro…