Reinforcement Learning

MAPS- A Metacognitive Architecture for Improved Perceptual and Social Learning: from simple tasks to multi-agent reinforcement learning
MAPS- A Metacognitive Architecture for Improved Perceptual and Social Learning: from simple tasks to multi-agent reinforcement learning

This study introduces the Metacognitive Architecture for Perceptual and Social Learning (MAPS), which integrates a second-order (metacognitive) network into AI systems to enhance social and continuous learning.

Apr 1, 2025

MAPLE: Modular Attention for Interpretable and Prosocial Multi-Agent Reinforcement Learning
MAPLE: Modular Attention for Interpretable and Prosocial Multi-Agent Reinforcement Learning

MAPLE introduces a novel approach to enhancing interpretability and performance in multi-agent reinforcement learning (MARL) through modular architecture and representation learning.

Mar 1, 2025