|
import pytest |
|
import torch |
|
|
|
from itertools import product |
|
from easydict import EasyDict |
|
from ding.world_model.mbpo import MBPOWorldModel |
|
from ding.utils import deep_merge_dicts |
|
|
|
|
|
state_size = [16] |
|
action_size = [16, 1] |
|
args = list(product(*[state_size, action_size])) |
|
|
|
|
|
@pytest.mark.unittest |
|
class TestMBPO: |
|
|
|
def get_world_model(self, state_size, action_size): |
|
cfg = MBPOWorldModel.default_config() |
|
cfg.model.max_epochs_since_update = 0 |
|
cfg = deep_merge_dicts( |
|
cfg, dict(cuda=False, model=dict(state_size=state_size, action_size=action_size, reward_size=1)) |
|
) |
|
fake_env = EasyDict(termination_fn=lambda obs: torch.zeros_like(obs.sum(-1)).bool()) |
|
return MBPOWorldModel(cfg, fake_env, None) |
|
|
|
@pytest.mark.parametrize('state_size, action_size', args) |
|
def test_step(self, state_size, action_size): |
|
states = torch.rand(128, state_size) |
|
actions = torch.rand(128, action_size) |
|
model = self.get_world_model(state_size, action_size) |
|
model.elite_model_idxes = [0, 1] |
|
rewards, next_obs, dones = model.step(states, actions) |
|
assert rewards.shape == (128, ) |
|
assert next_obs.shape == (128, state_size) |
|
assert dones.shape == (128, ) |
|
|
|
@pytest.mark.parametrize('state_size, action_size', args) |
|
def test_train(self, state_size, action_size): |
|
states = torch.rand(1280, state_size) |
|
actions = torch.rand(1280, action_size) |
|
|
|
next_states = states + actions.mean(1, keepdim=True) |
|
rewards = next_states.mean(1, keepdim=True) |
|
|
|
inputs = torch.cat([states, actions], dim=1) |
|
labels = torch.cat([rewards, next_states], dim=1) |
|
|
|
model = self.get_world_model(state_size, action_size) |
|
model._train(inputs[:64], labels[:64]) |
|
|
|
@pytest.mark.parametrize('state_size, action_size', args[:1]) |
|
def test_others(self, state_size, action_size): |
|
states = torch.rand(1280, state_size) |
|
actions = torch.rand(1280, action_size) |
|
|
|
next_states = states + actions.mean(1, keepdim=True) |
|
rewards = next_states.mean(1, keepdim=True) |
|
|
|
inputs = torch.cat([states, actions], dim=1) |
|
labels = torch.cat([rewards, next_states], dim=1) |
|
|
|
model = self.get_world_model(state_size, action_size) |
|
model._train(inputs[:64], labels[:64]) |
|
model._save_states() |
|
model._load_states() |
|
model._save_best(0, [1, 2, 3]) |
|
|
