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import torch
import numpy as np
import pytest
from itertools import product
from ding.model.template import DiscreteMAQAC, ContinuousMAQAC
from ding.torch_utils import is_differentiable
from ding.utils.default_helper import squeeze
B = 32
agent_obs_shape = [216, 265]
global_obs_shape = [264, 324]
agent_num = 8
action_shape = 14
args = list(product(*[agent_obs_shape, global_obs_shape, [False, True]]))
@pytest.mark.unittest
@pytest.mark.parametrize('agent_obs_shape, global_obs_shape, twin_critic', args)
class TestDiscreteMAQAC:
def output_check(self, model, outputs, action_shape):
if isinstance(action_shape, tuple):
loss = sum([t.sum() for t in outputs])
elif np.isscalar(action_shape):
loss = outputs.sum()
is_differentiable(loss, model)
def test_maqac(self, agent_obs_shape, global_obs_shape, twin_critic):
data = {
'obs': {
'agent_state': torch.randn(B, agent_num, agent_obs_shape),
'global_state': torch.randn(B, agent_num, global_obs_shape),
'action_mask': torch.randint(0, 2, size=(B, agent_num, action_shape))
}
}
model = DiscreteMAQAC(agent_obs_shape, global_obs_shape, action_shape, twin_critic=twin_critic)
logit = model(data, mode='compute_actor')['logit']
value = model(data, mode='compute_critic')['q_value']
value_sum = sum(t.sum() for t in value) if twin_critic else value.sum()
outputs = value_sum + logit.sum()
self.output_check(model, outputs, action_shape)
for p in model.parameters():
p.grad = None
logit = model(data, mode='compute_actor')['logit']
self.output_check(model.actor, logit, action_shape)
for p in model.parameters():
p.grad = None
value = model(data, mode='compute_critic')['q_value']
if twin_critic:
for v in value:
assert v.shape == (B, agent_num, action_shape)
else:
assert value.shape == (B, agent_num, action_shape)
self.output_check(model.critic, sum(t.sum() for t in value) if twin_critic else value.sum(), action_shape)
B = 32
agent_obs_shape = [216, 265]
global_obs_shape = [264, 324]
agent_num = 8
action_shape = 14
action_space = ['regression', 'reparameterization']
args = list(product(*[agent_obs_shape, global_obs_shape, action_space, [False, True]]))
@pytest.mark.unittest
@pytest.mark.parametrize('agent_obs_shape, global_obs_shape, action_space, twin_critic', args)
class TestContinuousMAQAC:
def output_check(self, model, outputs, action_shape):
if isinstance(action_shape, tuple):
loss = sum([t.sum() for t in outputs])
elif np.isscalar(action_shape):
loss = outputs.sum()
is_differentiable(loss, model)
def test_continuousmaqac(self, agent_obs_shape, global_obs_shape, action_space, twin_critic):
data = {
'obs': {
'agent_state': torch.randn(B, agent_num, agent_obs_shape),
'global_state': torch.randn(B, agent_num, global_obs_shape),
'action_mask': torch.randint(0, 2, size=(B, agent_num, action_shape))
},
'action': torch.randn(B, agent_num, squeeze(action_shape))
}
model = ContinuousMAQAC(agent_obs_shape, global_obs_shape, action_shape, action_space, twin_critic=twin_critic)
for p in model.parameters():
p.grad = None
if action_space == 'regression':
action = model(data['obs'], mode='compute_actor')['action']
if squeeze(action_shape) == 1:
assert action.shape == (B, )
else:
assert action.shape == (B, agent_num, squeeze(action_shape))
assert action.eq(action.clamp(-1, 1)).all()
self.output_check(model.actor, action, action_shape)
#is_differentiable(action.sum(), model.actor)
elif action_space == 'reparameterization':
(mu, sigma) = model(data['obs'], mode='compute_actor')['logit']
assert mu.shape == (B, agent_num, action_shape)
assert sigma.shape == (B, agent_num, action_shape)
is_differentiable(mu.sum() + sigma.sum(), model.actor)
for p in model.parameters():
p.grad = None
value = model(data, mode='compute_critic')['q_value']
if twin_critic:
for v in value:
assert v.shape == (B, agent_num)
else:
assert value.shape == (B, agent_num)
self.output_check(model.critic, sum(t.sum() for t in value) if twin_critic else value.sum(), action_shape)
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