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import pytest |
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import torch |
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from ding.torch_utils import is_differentiable |
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from ding.model.template import CollaQ |
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@pytest.mark.unittest |
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def test_collaQ(): |
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use_mixer = [True, False] |
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agent_num, bs, T = 4, 6, 8 |
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obs_dim, obs_alone_dim, global_obs_dim, action_dim = 32, 24, 32 * 4, 9 |
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self_feature_range = [8, 10] |
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allay_feature_range = [10, 16] |
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embedding_dim = 64 |
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for mix in use_mixer: |
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collaQ_model = CollaQ( |
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agent_num, |
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obs_dim, |
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obs_alone_dim, |
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global_obs_dim, |
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action_dim, [128, embedding_dim], |
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True, |
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self_feature_range, |
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allay_feature_range, |
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32, |
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mix, |
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activation=torch.nn.Tanh() |
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) |
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print(collaQ_model) |
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data = { |
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'obs': { |
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'agent_state': torch.randn(T, bs, agent_num, obs_dim), |
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'agent_alone_state': torch.randn(T, bs, agent_num, obs_alone_dim), |
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'agent_alone_padding_state': torch.randn(T, bs, agent_num, obs_dim), |
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'global_state': torch.randn(T, bs, global_obs_dim), |
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'action_mask': torch.randint(0, 2, size=(T, bs, agent_num, action_dim)) |
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}, |
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'prev_state': [[[None for _ in range(agent_num)] for _ in range(3)] for _ in range(bs)], |
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'action': torch.randint(0, action_dim, size=(T, bs, agent_num)) |
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} |
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output = collaQ_model(data, single_step=False) |
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assert set(output.keys()) == set(['total_q', 'logit', 'next_state', 'action_mask', 'agent_colla_alone_q']) |
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assert output['total_q'].shape == (T, bs) |
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assert output['logit'].shape == (T, bs, agent_num, action_dim) |
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assert len(output['next_state']) == bs and all([len(n) == 3 for n in output['next_state']]) and all( |
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[len(q) == agent_num for n in output['next_state'] for q in n] |
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) |
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print(output['next_state'][0][0][0]['h'].shape) |
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loss = output['total_q'].sum() |
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is_differentiable(loss, collaQ_model) |
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data.pop('action') |
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output = collaQ_model(data, single_step=False) |
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