Reinforcement Learning for distributing items on multiple places, i.e., scale balancing

I am interested in creating a reinforcement learning algorithm for assigning items to different buckets, such that the buckets are almost the same weight, i.e., scale but with more than two places to distribute weight.

Each item can have a certain weight, each bucket can but also doesn't need to have a maximal load, however, all buckets need to have almost the same weight in the end. For instance, if we have 5 items with weights [1,2,3,6,5] and three buckets a good distribution would be to have [1,2,3] [6] [5].

Is there a common name for this problem?

Additionally, if using gym, would the observation and state be discrete or continuous and how will the general Environment roughly look like?