Search Results for author: Alexander Trott

Found 9 papers, 6 papers with code

Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal

1 code implementation18 Jan 2022 Tong Mu, Stephan Zheng, Alexander Trott

In a sequential setting with multiple Agents, RIRL shows opposing consequences of the Principal's inattention to different information channels: 1) inattention to Agents' outputs closes wage gaps based on ability differences; and 2) inattention to Agents' efforts induces a social dilemma dynamic in which Agents work harder, but essentially for free.

Multi-agent Reinforcement Learning

Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning

no code implementations3 Jan 2022 Michael Curry, Alexander Trott, Soham Phade, Yu Bai, Stephan Zheng

We validate the learned solutions are $\epsilon$-meta-equilibria through best-response analyses, show that they align with economic intuitions, and show our approach can learn a spectrum of qualitatively distinct $\epsilon$-meta-equilibria in open RBC models.

Multi-agent Reinforcement Learning reinforcement-learning +1

Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist

1 code implementation6 Aug 2021 Alexander Trott, Sunil Srinivasa, Douwe van der Wal, Sebastien Haneuse, Stephan Zheng

Here we show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning (RL) and data-driven simulations.

Reinforcement Learning (RL)

The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning

1 code implementation5 Aug 2021 Stephan Zheng, Alexander Trott, Sunil Srinivasa, David C. Parkes, Richard Socher

Here we show that machine-learning-based economic simulation is a powerful policy and mechanism design framework to overcome these limitations.

counterfactual reinforcement-learning +1

The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies

2 code implementations28 Apr 2020 Stephan Zheng, Alexander Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David C. Parkes, Richard Socher

In experiments conducted on MTurk, an AI tax policy provides an equality-productivity trade-off that is similar to that provided by the Saez framework along with higher inverse-income weighted social welfare.

Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills

1 code implementation ICML 2020 Víctor Campos, Alexander Trott, Caiming Xiong, Richard Socher, Xavier Giro-i-Nieto, Jordi Torres

We perform an extensive evaluation of skill discovery methods on controlled environments and show that EDL offers significant advantages, such as overcoming the coverage problem, reducing the dependence of learned skills on the initial state, and allowing the user to define a prior over which behaviors should be learned.

Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards

1 code implementation NeurIPS 2019 Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher

For instance, in tasks where the agent must achieve some goal state, simple distance-to-goal reward shaping often fails, as it renders learning vulnerable to local optima.

Competitive Experience Replay

no code implementations ICLR 2019 Hao Liu, Alexander Trott, Richard Socher, Caiming Xiong

We propose a novel method called competitive experience replay, which efficiently supplements a sparse reward by placing learning in the context of an exploration competition between a pair of agents.

reinforcement-learning Reinforcement Learning (RL)

Interpretable Counting for Visual Question Answering

no code implementations ICLR 2018 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).

Question Answering Visual Question Answering

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