Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning
Recently, deep Multi-Agent Reinforcement Learning (MARL) has demonstrated its potential
to tackle complex cooperative tasks, pushing the boundaries of AI in collaborative …
to tackle complex cooperative tasks, pushing the boundaries of AI in collaborative …
SPEQ: Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learning
A key challenge in Deep Reinforcement Learning is sample efficiency, especially in real-
world applications where collecting environment interactions is expensive or risky. Recent …
world applications where collecting environment interactions is expensive or risky. Recent …