Hi, @Jogima-cyber ! Long time no see, and thanks for your great suggestion!
I also tried the multiple action situation this year, so I'll check if it can be put in the main code.

I'm currently working on it ! First thing I'm doing right now is integrating a home made simple multidiscrete environment into HandyRL in order to test my upcoming multidicrete integration. I'll push it as soon as I'm finished (it's gonna include a non-multidiscrete option).
I'm not sur I'll need to add a multidiscrete variable into config, maybe just nvec being defined in model should be enough for the library to know it should handle a multidiscrete case.
I need this feature right now, so I figured I could add it globally instead on just my fork, if you want it though.

@Jogima-cyber
I pushed feature/multi_unit branch into my fork.
master...YuriCat:HandyRL:feature/multi_unit

In this branch, each agent can output env.num_units() actions in each turn.
I've confirmed that it works with TicTacToe!

Thank you @YuriCat, that's almost exactly what I needed. I'm working on your fork, I just need to add a moment "unit_mask", because some of my independent action sets may be empty depending on the state (meaning no action can be taken among the actions of the action set).

@Jogima-cyber

unit_mask

Indeed! We need unit_mask if there are absent units.

Btw. I've output the shapes of some vectors and in line feature/multi_unit/train.py:l204 I've got shapes that I don't think are normal :

• for total_advantages I got torch.Size([1, 32, 1, 2304, 2304])
• meaning that for (-log_selected_policies * total_advantages) I got torch.Size([1, 32, 1, 2304, 2304])
  Where 32 is my BS and 2304 is my unit number.
  I'm investigating to understand more parts of your code in order to know if it's normal behaviour or not.

@Jogima-cyber
.unsqueeze(-1) may be necessary for either of the two.

Yeah I found where to add a .unsqueeze(-1) : feature/multi_unit/train.py:l234 we have log_rhos = log_selected_t_policies.detach() - log_selected_b_policies but log_selected_t_policies is of shape (1, 32, 1, 2304, 1) but log_selected_b_policies is of shape (1, 32, 1, 1, 2304) and therefore torch is applying broadcasting. I've solved the issue by adding .unsqueeze(-1) and it should work for my case but I didn't take into account all the cases HandyRL is covering like RNN, turn based training and so on, so I don't think my fixing apply generally for HandyRL.

@Jogima-cyber Updated my branch feature/numti_unit.