Interpretable Counting for Visual Question Answering. - Alexander Trott; Caiming Xiong; Richard Socher (ICLR (Poster), 2018)

Competitive experience replay. - Hao Liu; Alexander Trott; Richard Socher; Caiming Xiong (ICLR (Poster), 2019)

Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards. - Alexander Trott; Stephan Zheng; Caiming Xiong; Richard Socher (NeurIPS, 2019)

Learning World Graphs to Accelerate Hierarchical Reinforcement Learning. - Wenling Shang; Alexander Trott; Stephan Zheng; Caiming Xiong; Richard Socher (CoRR, 2019)

Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills. - Victor Campos 001; Alexander Trott; Caiming Xiong; Richard Socher; Xavier Giró-i-Nieto; Jordi Torres (ICML, 2020)

The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies. - Stephan Zheng; Alexander Trott; Sunil Srinivasa; Nikhil Naik 002; Melvin Gruesbeck; David C. Parkes; Richard Socher (CoRR, 2020)

ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations. - Eric Zhao 003; Alexander R. Trott; Caiming Xiong; Stephan Zheng (CoRR, 2021)

The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning. - Stephan Zheng; Alexander Trott; Sunil Srinivasa; David C. Parkes; Richard Socher (CoRR, 2021)

Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist. - Alexander Trott; Sunil Srinivasa; Douwe van der Wal; Sebastien Haneuse; Stephan Zheng (CoRR, 2021)

Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning. - Michael Curry 002; Alexander Trott; Soham Phade; Yu Bai; Stephan Zheng (CoRR, 2022)

Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal. - Tong Mu; Stephan Zheng; Alexander Trott (CoRR, 2022)

Using Reinforcement Learning to Study Platform Economies under Market Shocks. - Xintong Wang 002; Gary Qiurui Ma; Alon Eden; Clara Li; Alexander Trott; Stephan Zheng; David C. Parkes (CoRR, 2022)

Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning. - Tong Mu; Stephan Zheng; Alexander R. Trott (Trans. Mach. Learn. Res., 2022)

Learning to Play General-Sum Games against Multiple Boundedly Rational Agents. - Eric Zhao 003; Alexander R. Trott; Caiming Xiong; Stephan Zheng (AAAI, 2023)

Learning Solutions in Large Economic Networks using Deep Multi-Agent Reinforcement Learning. - Michael Curry 002; Alexander Trott; Soham Phade; Yu Bai; Stephan Zheng (AAMAS, 2023)

MosaicBERT: A Bidirectional Encoder Optimized for Fast Pretraining. - Jacob P. Portes; Alexander Trott; Sam Havens; Daniel King; Abhinav Venigalla; Moin Nadeem; Nikhil Sardana; Daya Khudia; Jonathan Frankle (NeurIPS, 2023)

Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study. - Xintong Wang 002; Gary Qiurui Ma; Alon Eden; Clara Li; Alexander Trott; Stephan Zheng; David C. Parkes (WWW, 2023)

LIMIT: Less Is More for Instruction Tuning Across Evaluation Paradigms. - Aditi Jha; Sam Havens; Jeremy Dohmann; Alex Trott; Jacob P. Portes (CoRR, 2023)