Agents
GUIDE: Real-Time Human-Shaped Agents
Due to their inherent complexity, these tasks pose significant challenges for current machine learning systems.
Going Beyond Heuristics by Imposing Policy Improvement as a Constraint Chi-Chang Lee
As such, we prevent policies from merely exploiting heuristic rewards without improving the task reward.
Towards Effective Planning Strategies for Dynamic Opinion Networks
Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks.
Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games Fanqi Kong 2, 1 Yizhe Huang
Real-world multi-agent scenarios often involve mixed motives, demanding altruistic agents capable of self-protection against potential exploitation.
Improving Environment Novelty Quantification for Effective Unsupervised Environment Design
Unsupervised Environment Design (UED) formalizes the problem of autocur-ricula through interactive training between a teacher agent and a student agent. The teacher generates new training environments with high learning potential, curating an adaptive curriculum that strengthens the student's ability to handle unseen scenarios. Existing UED methods mainly rely on regret, a metric that measures the difference between the agent's optimal and actual performance, to
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
Input reduction reduces the length of the input context before feeding to downstream LLMs.
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models
The in-context learning (ICL) capability of pre-trained models based on the transformer architecture has received growing interest in recent years.