Agents
Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation
This paper proceeds as follows.
GUIDE: Real-Time Human-Shaped Agents
Due to their inherent complexity, these tasks pose significant challenges for current machine learning systems.
Scalable Constrained Policy Optimization for Safe Multi-agent Reinforcement Learning
A challenging problem in seeking to bring multi-agent reinforcement learning (MARL) techniques into real-world applications, such as autonomous driving and drone swarms, is how to control multiple agents safely and cooperatively to accomplish tasks.
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.
WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control
WFCRL also offers turbine load observations that will allow to optimize the farm performance while limiting turbine structural damages.