Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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45 行
1.8 KiB

import numpy as np
from mlagents.trainers.buffer import Buffer
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)
c = b.update_buffer.make_mini_batch(start=0, end=1)
assert c.keys() == b.update_buffer.keys()
assert c['action'].shape == (1, 2, 2)