Cobalt is widely used in batteries and metal composite materials, and the cobalt oxalate synthesis process is a key process that affects product quality. To optimize the average particle size of cobalt oxalate, this work presents an optimization method for the cobalt oxalate synthesis process based on the modified proximal policy optimization (MPPO) algorithm. First, the reward function is designed according to the optimization objectives and constraints of the cobalt oxalate synthesis process, and the optimization problem is incorporated into the reinforcement learning framework by establishing the Markov decision model of the process. Secondly, to deal with the problem of gradient disappearance in the training process of the policy network, the residual network is proposed as the policy network of the PPO algorithm. Finally, to avoid the PPO algorithm falling into the local optimal strategy due to the continuous state space of the process, the initial strategy is improved by interleaved imitation learning. The proposed method is compared with the traditional PPO algorithm, and the modified PPO algorithm has a better optimization effect and convergence while satisfying the constraints.