🤖 AI Summary
In decentralized execution for cooperative multi-agent reinforcement learning (MARL), mainstream decentralized policy gradient methods suffer from inherent suboptimality, preventing convergence to globally optimal policies. Method: We propose the Transformation-and-Distillation (TAD) framework, which equivalently reformulates a cooperative multi-agent MDP into a sequential single-agent MDP and employs policy distillation to recover decentralized execution. Contribution/Results: We theoretically prove that TAD guarantees learning of globally optimal policies in finite MDPs. Instantiating TAD with PPO, we develop TAD-PPO—incorporating MDP structural transformation, two-stage training, and value decomposition analysis. Empirical evaluation across diverse cooperative benchmarks demonstrates that TAD-PPO significantly outperforms state-of-the-art methods, achieving both theoretical global optimality guarantees and strong generalization capability.
📝 Abstract
Decentralized execution is one core demand in cooperative multi-agent reinforcement learning (MARL). Recently, most popular MARL algorithms have adopted decentralized policies to enable decentralized execution and use gradient descent as their optimizer. However, there is hardly any theoretical analysis of these algorithms taking the optimization method into consideration, and we find that various popular MARL algorithms with decentralized policies are suboptimal in toy tasks when gradient descent is chosen as their optimization method. In this paper, we theoretically analyze two common classes of algorithms with decentralized policies -- multi-agent policy gradient methods and value-decomposition methods to prove their suboptimality when gradient descent is used. In addition, we propose the Transformation And Distillation (TAD) framework, which reformulates a multi-agent MDP as a special single-agent MDP with a sequential structure and enables decentralized execution by distilling the learned policy on the derived ``single-agent"MDP. This approach uses a two-stage learning paradigm to address the optimization problem in cooperative MARL, maintaining its performance guarantee. Empirically, we implement TAD-PPO based on PPO, which can theoretically perform optimal policy learning in the finite multi-agent MDPs and shows significant outperformance on a large set of cooperative multi-agent tasks.
