44 citations · 144 across the 12 of their papers we have counts for
12 papers
Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study
Xintong Wang, Gary Qiurui Ma, Alon Eden +4
We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations i…
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning
Michael Curry, Alexander Trott, Soham Phade +2
Real economies can be modeled as a sequential imperfect-information game with many heterogeneous agents, such as consumers, firms, and governments. Dynamic general equilibrium (DGE…
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal
Tong Mu, Stephan Zheng, Alexander Trott
Principal-Agent (PA) problems describe a broad class of economic relationships characterized by misaligned incentives and asymmetric information. The Principal's problem is to find…
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…
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
Stephan Zheng, Alexander Trott, Sunil Srinivasa +4
Tackling real-world socio-economic challenges requires designing and testing economic policies. However, this is hard in practice, due to a lack of appropriate (micro-level) econom…
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…
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
Alexander Trott, Stephan Zheng, Caiming Xiong +1
While using shaped rewards can be beneficial when solving sparse reward tasks, their successful application often requires careful engineering and is problem specific. For instance…
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…
Interpretable Counting for Visual Question Answering
Alexander Trott, Caiming Xiong, Richard Socher
Questions that require counting a variety of objects in images remain a major challenge in visual question answering (VQA). The most common approaches to VQA involve either classif…