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import pytest
import torch
from ding.torch_utils import is_differentiable
from ding.model.template import CollaQ
@pytest.mark.unittest
def test_collaQ():
use_mixer = [True, False]
agent_num, bs, T = 4, 6, 8
obs_dim, obs_alone_dim, global_obs_dim, action_dim = 32, 24, 32 * 4, 9
self_feature_range = [8, 10]
allay_feature_range = [10, 16]
embedding_dim = 64
for mix in use_mixer:
collaQ_model = CollaQ(
agent_num,
obs_dim,
obs_alone_dim,
global_obs_dim,
action_dim, [128, embedding_dim],
True,
self_feature_range,
allay_feature_range,
32,
mix,
activation=torch.nn.Tanh()
)
print(collaQ_model)
data = {
'obs': {
'agent_state': torch.randn(T, bs, agent_num, obs_dim),
'agent_alone_state': torch.randn(T, bs, agent_num, obs_alone_dim),
'agent_alone_padding_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(3)] for _ in range(bs)],
'action': torch.randint(0, action_dim, size=(T, bs, agent_num))
}
output = collaQ_model(data, single_step=False)
assert set(output.keys()) == set(['total_q', 'logit', 'next_state', 'action_mask', 'agent_colla_alone_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) == 3 for n in output['next_state']]) and all(
[len(q) == agent_num for n in output['next_state'] for q in n]
)
print(output['next_state'][0][0][0]['h'].shape)
# data['prev_state'] = output['next_state']
loss = output['total_q'].sum()
is_differentiable(loss, collaQ_model)
data.pop('action')
output = collaQ_model(data, single_step=False)
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