您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
56 行
2.2 KiB
56 行
2.2 KiB
import json
|
|
import unittest.mock as mock
|
|
|
|
import yaml
|
|
import pytest
|
|
import numpy as np
|
|
|
|
from unitytrainers.trainer_controller import TrainerController
|
|
from unitytrainers.buffer import Buffer
|
|
from unitytrainers.ppo.trainer import PPOTrainer
|
|
from unitytrainers.bc.trainer import BehavioralCloningTrainer
|
|
from unitytrainers.curriculum import Curriculum
|
|
from unitytrainers.exception import CurriculumError
|
|
from unityagents.exception import UnityEnvironmentException
|
|
from .mock_communicator import MockCommunicator
|
|
|
|
|
|
def assert_array(a, b):
|
|
assert a.shape == b.shape
|
|
la = list(a.flatten())
|
|
lb = list(b.flatten())
|
|
for i in range(len(la)):
|
|
assert la[i] == lb[i]
|
|
|
|
|
|
def test_buffer():
|
|
b = Buffer()
|
|
for fake_agent_id in range(4):
|
|
for step in range(9):
|
|
b[fake_agent_id]['vector_observation'].append(
|
|
[100 * fake_agent_id + 10 * step + 1,
|
|
100 * fake_agent_id + 10 * step + 2,
|
|
100 * fake_agent_id + 10 * step + 3]
|
|
)
|
|
b[fake_agent_id]['action'].append([100 * fake_agent_id + 10 * step + 4,
|
|
100 * fake_agent_id + 10 * step + 5])
|
|
a = b[1]['vector_observation'].get_batch(batch_size=2, training_length=1, sequential=True)
|
|
assert_array(a, np.array([[171, 172, 173], [181, 182, 183]]))
|
|
a = b[2]['vector_observation'].get_batch(batch_size=2, training_length=3, sequential=True)
|
|
assert_array(a, np.array([
|
|
[[231, 232, 233], [241, 242, 243], [251, 252, 253]],
|
|
[[261, 262, 263], [271, 272, 273], [281, 282, 283]]
|
|
]))
|
|
a = b[2]['vector_observation'].get_batch(batch_size=2, training_length=3, sequential=False)
|
|
assert_array(a, np.array([
|
|
[[251, 252, 253], [261, 262, 263], [271, 272, 273]],
|
|
[[261, 262, 263], [271, 272, 273], [281, 282, 283]]
|
|
]))
|
|
b[4].reset_agent()
|
|
assert len(b[4]) == 0
|
|
b.append_update_buffer(3,
|
|
batch_size=None, training_length=2)
|
|
b.append_update_buffer(2,
|
|
batch_size=None, training_length=2)
|
|
assert len(b.update_buffer['action']) == 10
|
|
assert np.array(b.update_buffer['action']).shape == (10, 2, 2)
|