Optimizing the loading of autoclaves is a critical 2D packing problem in aerospace manufacturing. This paper investigates using deep reinforcement learning (DRL), specifically the proximal policy optimization (PPO) algorithm, to learn effective packing strategies. We developed a custom OpenAI Gym environment that simulates the process, with an observation space that details three matrices representing the initial and final states of the board, the shape of the item and the quantities of the remaining items, a multidiscrete action space to select and place the items, and a reward function that incorporates item profit and invalid action penalties. We evaluated our PPO-based agent against a genetic algorithm (GA) across scenarios involving regular items, mixed regular/irregular items, and item prioritization based on size. In the simplest case with only regular items, PPO achieved a high fill rate (99.37%), slightly surpassing GA (97.50%). In more complex scenarios involving mixed shapes and item prioritization, the GA achieved board fill rates of 94.57% and 96.57%, while PPO reached only 90.00% and 93.75%. However, in our experiments, PPO correctly move a greater number of the prioritized items in total. Prioritizing specific items is a critical task in the autoclave context, and highlighting PPO is more capable of learning value-driven allocation strategies.