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- Using Social Sciences to Enhance the Realism of Simulation for Complex Urban Environments4
The team, composed of engineers and social scientists, describe here their approach toward tackling complex simulation problems with embedded human factors and some of the results obtained are presented.
- Training Reinforcement Learning Agents and Humans With Difficulty-Conditioned Generators2
This work adapts Parameterized Environment Response Model (PERM), a method for training both Reinforcement Learning Agents and human learners in parameterized environments by directly modeling difficulty and ability, and demonstrates its effectiveness in training RL agents and humans in an empirical study.
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- Hey Chat, Can You Teach Me? Structuring Socratic Dialogue for Human Learning in the Wild–
Across held-out STEM and non-STEM topics, the PPO-paired tutor outperforms heuristic baselines, frontier general-purpose models, and a model specialised for Socratic dialogue: on both the rate at which students reach full curriculum mastery and the number of turns required.
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- Efficient Unsupervised Environment Design through Hierarchical Policy Representation Learning–
This work introduces a hierarchical Markov Decision Process (MDP) framework for environment design that features a teacher agent that leverages student policy representations derived from discovered evaluation environments, enabling it to generate training environments based on the student's capabilities.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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