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import pytest
from itertools import product
import torch
from ding.model.template import QTran
from ding.torch_utils import is_differentiable
@pytest.mark.unittest
def test_qtran():
agent_num, bs, T = 4, 3, 8
obs_dim, global_obs_dim, action_dim = 32, 32 * 4, 9
embedding_dim = 64
data = {
'obs': {
'agent_state': torch.randn(T, bs, agent_num, obs_dim),
'global_state': torch.randn(T, bs, global_obs_dim),
'action_mask': torch.randint(0, 2, size=(T, bs, agent_num, action_dim))
},
'prev_state': [[None for _ in range(agent_num)] for _ in range(bs)],
'action': torch.randint(0, action_dim, size=(T, bs, agent_num))
}
model = QTran(agent_num, obs_dim, global_obs_dim, action_dim, [32, embedding_dim], embedding_dim)
output = model.forward(data, single_step=False)
assert set(output.keys()) == set(['next_state', 'agent_q_act', 'vs', 'logit', 'action_mask', 'total_q'])
assert output['total_q'].shape == (T, bs)
assert output['logit'].shape == (T, bs, agent_num, action_dim)
assert len(output['next_state']) == bs and all([len(n) == agent_num for n in output['next_state']])
print(output['next_state'][0][0]['h'].shape)
loss = output['total_q'].sum() + output['agent_q_act'].sum() + output['vs'].sum()
is_differentiable(loss, model)
data.pop('action')
outputs = model.forward(data, single_step=False)
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