FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Existing multiagent proximal policy optimization (PPO) methods struggle with generalizability across diverse parameter-sharing strategies (full, partial, nonparameter sharing).
• Extending PPO's theoretical guarantees to cooperative multiagent reinforcement learning (MARL) remains a significant challenge.

Purpose of the Study:

• To introduce a general-purpose method for multiagent PPO that overcomes limitations in parameter-sharing versatility.
• To develop a theoretically sound and practically effective algorithm for cooperative MARL.

Main Methods:

• Proposed the 'full-pipeline paradigm' utilizing equivalent advantage function decompositions.
• Established multiple parallel optimization pipelines for enhanced flexibility.
• Developed the practical algorithm 'full-pipeline PPO' (FP3O).

Main Results:

• FP3O demonstrates consistent policy improvement across various parameter-sharing configurations.
• Theoretical analysis confirms alignment between theory and practice for the proposed method.
• Empirical evaluations show FP3O outperforms existing baselines on multiple MARL tasks.

Conclusions:

• FP3O offers a versatile and effective solution for multiagent PPO, addressing generalizability issues.
• The method provides a theoretically and practically aligned guarantee for cooperative MARL.
• FP3O exhibits remarkable versatility across all common parameter-sharing standards.