Collaborative Reinforcement Learning

Collaborative reinforcement learning (CRL) focuses on training multiple agents to cooperate and achieve a shared goal, addressing challenges like credit assignment and robustness in complex, multi-agent environments. Current research emphasizes developing efficient algorithms, such as variations of reinforcement learning (e.g., proximal policy optimization) and reward shaping techniques (e.g., potential-based methods), to improve coordination and performance in diverse applications. CRL's significance lies in its ability to solve intricate problems requiring coordinated action, with applications ranging from autonomous vehicle testing and UAV control to human-robot interaction and metaverse development.