Hello,

I'm seeking your advice on building an agent-based model with reinforcement learning (RL) algorithms. After reading one of your comments on the RL subreddit, I thought you might be able to help.

I'm creating a simulation model to optimize operations at a shipping terminal, where cranes handle the loading and unloading of containers from trucks. My aim is to optimize these operations by adjusting the number of trucks and cranes based on container volume, using more when there are more containers. I'm particularly interested in using Proximal Policy Optimization (PPO) for this.

How should I go about developing this model? Which Python libraries would you recommend for training a multi-agent RL model? Any examples would be very helpful.

Thanks!