Q-CMAPO: A quantum-classical framework for balancing exploration and exploitation in multi-agent reinforcement learning

Related papers

Ranked by semantic similarity — how closely each paper's abstract matches this one (100% = near-identical topic).

• Self-Motivated Multi-Agent Exploration (2023)71% match
• Optimistic ε-greedy Exploration For Cooperative Multi-agent Reinforcement Learning (2026)69% match
• MAexp: A Generic Platform for RL-based Multi-Agent Exploration (2024)69% match
• MAVEN: Multi-Agent Variational Exploration (2019)69% match
• Coordinated Exploration Via Intrinsic Rewards For Multi-agent Reinforcement Learning (2019)68% match
• Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning (2023)68% match
• Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning (2019)68% match
• Conditionally Optimistic Exploration For Cooperative Deep Multi-agent Reinforcement Learning (2023)68% match