Dynamic Reinforcement Learning Decision Architecture

Develop a comprehensive reinforcement learning framework capable of making adaptive decisions in complex, partially observable environments. Create an architecture combining deep reinforcement learning, meta-learning techniques, and advanced exploration strategies that can generalize across different problem domains. Include specific methodologies for handling reward sparsity, managing long-term credit assignment, and maintaining stable learning dynamics.