The increasing number of satellites in low Earth orbit has significantly heightened the risk of collisions between space objects. Servicing and debris removal missions offer a viable solution by extending satellite lifespans and clearing orbital pathways. This research presents an innovative approach for spacecraft guidance and control in six degrees-of-freedom orbital rendezvous scenarios, employing meta-reinforcement learning and transformer networks. Leveraging transformer networks, this model enables the chaser spacecraft to learn complex temporal relationships and infer hidden information from the environment. The Proximal Policy Optimization (PPO) algorithm, utilized for model training, demonstrates superior performance in continuous control tasks. Simulation results in a virtual environment indicate that this approach outperforms traditional architectures like LSTM in terms of accuracy and stability. Additionally, network parameter count poses a significant challenge for hardware implementation; the proposed method addresses this by achieving substantial parameter reduction alongside enhanced adaptability and improved precision under varying environmental conditions. This approach could serve as an effective solution for facilitating future on-orbit servicing and space debris management missions.