Dynamic Reinforcement Learning Decision Optimization Framework

Design a flexible reinforcement learning framework for complex decision optimization problems with multiple competing objectives. Implement an advanced multi-agent system using deep reinforcement learning techniques like proximal policy optimization and actor-critic methods. Develop a modular architecture supporting adaptive learning rates, exploration-exploitation strategies, and comprehensive performance tracking. Include robust simulation environments, hyperparameter tuning mechanisms, and interpretable policy visualization tools.