Edge caching is a technology that stores data at the edge of the network. Currently, collaborative edge caching has become an effective solution to reduce content transmission latency and alleviate network congestion. However, the complexity and unreliability of real-world network environments can cause intermittent failures at edge nodes. These unreliable factors may lead to a decrease in cache hit rates and directly impact the Quality of Experience (QoE) for users. Ensuring service continuity under frequently fluctuating network conditions and making optimal collaborative caching decisions in uncertain environments is a challenging problem. The paper proposes a Multi-Agent Graph Attention Reinforcement Learning (MAGARL) approach for edge collaborative caching, aiming to address the challenge of content collaboration caching in environments with node failures. MAGARL leverages the graph attention mechanism to capture dynamic topology changes in the multi-agent environment, enabling agents to develop robust collaborative caching strategies through reinforcement learning training under dynamic and fluctuating conditions. Through simulation experiments, MAGARL was tested in scenarios considering node failures to verify its effectiveness. Compared to baseline algorithms, MAGARL achieved an average 3.71% increase in cache hit rate and a 10.31% reduction in latency.