Evaluation issues

[4e4ako]

Hi Vsevolod!

I've tried to launch PLO_training_start.py with enabled LBR and failed (without any eval_methods iterations are running fine, but I can't evaluate results). I've tried both PLO and DiscretizedNLHoldem, with Debugging option turned on and off.
When DEBUGGING=True, and nn_type "feedforward" or "dense_residual", I've got AssertionError:

/PokerRL-Omaha-master/DeepCFR/IterationStrategy.py", line 144, in get_a_probs_for_each_hand_in_list
assert len(pub_obs.shape) == 2, "all hands have the same public obs"
AssertionError: all hands have the same public obs

And if DEBUGGING=False I've got this error on iteration 1:

PokerRL-Omaha-master/PokerRL/rl/neural/MainPokerModuleFLAT2.py", line 109, in forward
pf_mask = torch.where(pub_obses[:, 14] == 1)
TypeError: list indices must be integers or slices, not tuple

If nn_type="recurrent", I've got error on iteration 0:

PokerRL-Omaha-master/PokerRL/rl/neural/MainPokerModuleRNN.py", line 157, in forward
pub_obses = torch.from_numpy(pub_obses[0]).to(self.device).view(seq_len, bs, self.pub_obs_size)
TypeError: expected np.ndarray (got Tensor)

My requirements.txt:

gym==0.10.9 (tried 0.12.5 too)
numpy==1.21.2
psutil==5.8.0
pycrayon==0.5
pytz==2021.3
ray==0.6.1 (didn't use Distributed)
scipy==1.7.3
torch==1.4.0 (tried Pytorch versions till 1.10 with CUDA 10.2)

[diditforlulz273]

Hi!
I think recurrent networks are legacy-type and never worked correctly, as they gave quite poor results on initial testing, so simply don't use them.

And this is clearly not a package dependencies problem - I guess, the reason is bugs in code - my local version is some commits ahead, and probably this is fixed there. But the problem is that I gave up on python around a year ago and rewritten it from scratch on C++ which is a whole different project.
Therefore, I don't have any incentive to go back and check what's actually wrong or fix it, sorry, but you have to do it yourself :)