• Publications
  • Influence
Interpretable Counting for Visual Question Answering
TLDR
The model sequentially selects from detected objects and learns interactions between objects that influence subsequent selections and outperforms the state of the art architecture for VQA on multiple metrics that evaluate counting. Expand
Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
TLDR
This work performs an extensive evaluation of skill discovery methods on controlled environments and shows 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. Expand
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
TLDR
This work introduces a simple and effective model-free method to learn from shaped distance-to-goal rewards on tasks where success depends on reaching a goal state and introduces an auxiliary distance-based reward based on pairs of rollouts to encourage diverse exploration. Expand
Competitive Experience Replay
TLDR
This work proposes 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, creating a competitive game designed to drive exploration. Expand
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
TLDR
This work proposes a two-level deep reinforcement learning approach to learn dynamic tax policies, based on economic simulations in which both agents and a government learn and adapt, and shows that AI-driven tax policies perform strongly in the face of emergent tax-gaming strategies learned by AI agents. Expand
Learning World Graphs to Accelerate Hierarchical Reinforcement Learning
TLDR
A thorough ablation study is performed to evaluate the proposed graph abstraction over the environment structure to accelerate the learning of these tasks with significant advantages from the proposed framework over baselines that lack world graph knowledge in terms of performance and efficiency. Expand
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning
TLDR
It is demonstrated for the first time that two-level, deep RL can be used for understanding and as a complement to theory for economic design, unlocking a new computational learning-based approach to understanding economic policy. Expand
Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist
TLDR
The AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning (RL) and data-driven simulations, and finds that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes. Expand
C OMPETITIVE EXPERIENCE REPLAY
Deep learning has achieved remarkable successes in solving challenging reinforcement learning (RL) problems. However, it still often suffers from the need to engineer a reward function that not onlyExpand
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