Unmanned aerial vehicle (UAV) assisted communications have been regarded as an effective solution to provide instant services. This paper proposes a novel reconfigurable intelligent surface (RIS) assisted multi-UAV system, where ground users are formed as multicast groups and served by multiple UAVs with coordinated multi-point technique. The goal is to maximize the sum of the minimum rates for all groups by jointly optimizing the trajectories, the cooperative beamforming of the clustered UAVs, and the passive beamforming of the RIS. A hybrid learning scheme is proposed, integrating a multi-agent deep reinforcement learning algorithm, RES-QMIX, and a majorization-minimization (MM)-based alternating optimization. First, the RES-QMIX algorithm is proposed to optimize the trajectories of all UAVs. Then, the alternating optimization is invoked to decouple the joint beamforming into two sub-problems, and each is transformed into a convex quadratic cone programming problem with the MM algorithm. Moreover, the alternating optimization is employed to estimate the reward of the action in RES-QMIX algorithm, thus reducing the action space and achieving the joint optimization. Numerical results show that: 1) The proposed hybrid learning framework achieves fast convergence and outperforms heuristic algorithms; 2) The proposed system obtains a more significant communication rate than CoMP and RIS-only systems.