Trust and reputation assessment plays a major role in multi-agent systems to enable seamless and useful interaction among agents. The assessment faces significant challenges due to the volatility of the environment as the agents evolve over time leading to colluding interactions. Such an ecosystem necessitates that the agents perform adaptive learning and have robust privacy mechanisms to ensure that the Trust and reputation thus assessed are context-aware as well. This paper presents a novel and holistic framework that addresses the challenges of privacy and adaptability by uniquely integrating Reinforcement Learning (RL) with Distributed Online Life-Long Learning (DOL3), graph clustering, and Graph Neural Networks (GNN). RL is induced to optimize trust learning strategies within DOL3, enhancing the adaptability of both collusion detection (through Graph Clustering) and privacy-preserving trust score learning (through GNN with differential privacy). This synergistic integration makes the framework more resilient to dynamic threats, and RL actively optimizes how trust is learned and applied within the DOL3 framework along with continuous privacy protection. This, essentially, offers enhanced adaptability, accuracy, and privacy-protection collusion detection. We demonstrate the potential of this framework to improve decision-making in complex domains such as autonomous systems, healthcare, and e-commerce.