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119 行
3.9 KiB
119 行
3.9 KiB
import numpy as np
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from mlagents.trainers.buffer import AgentProcessorBuffer, AgentBuffer
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def assert_array(a, b):
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assert a.shape == b.shape
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la = list(a.flatten())
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lb = list(b.flatten())
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for i in range(len(la)):
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assert la[i] == lb[i]
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def construct_fake_processing_buffer():
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b = AgentProcessorBuffer()
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for fake_agent_id in range(4):
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for step in range(9):
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b[fake_agent_id]["vector_observation"].append(
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[
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100 * fake_agent_id + 10 * step + 1,
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100 * fake_agent_id + 10 * step + 2,
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100 * fake_agent_id + 10 * step + 3,
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]
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)
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b[fake_agent_id]["action"].append(
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[
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100 * fake_agent_id + 10 * step + 4,
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100 * fake_agent_id + 10 * step + 5,
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]
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)
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return b
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def test_buffer():
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b = construct_fake_processing_buffer()
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a = b[1]["vector_observation"].get_batch(
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batch_size=2, training_length=1, sequential=True
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)
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assert_array(np.array(a), np.array([[171, 172, 173], [181, 182, 183]]))
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a = b[2]["vector_observation"].get_batch(
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batch_size=2, training_length=3, sequential=True
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)
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assert_array(
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np.array(a),
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np.array(
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[
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[231, 232, 233],
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[241, 242, 243],
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[251, 252, 253],
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[261, 262, 263],
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[271, 272, 273],
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[281, 282, 283],
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]
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),
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)
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a = b[2]["vector_observation"].get_batch(
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batch_size=2, training_length=3, sequential=False
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)
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assert_array(
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np.array(a),
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np.array(
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[
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[251, 252, 253],
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[261, 262, 263],
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[271, 272, 273],
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[261, 262, 263],
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[271, 272, 273],
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[281, 282, 283],
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]
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),
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)
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b[4].reset_agent()
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assert len(b[4]) == 0
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update_buffer = AgentBuffer()
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b.append_to_update_buffer(update_buffer, 3, batch_size=None, training_length=2)
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b.append_to_update_buffer(update_buffer, 2, batch_size=None, training_length=2)
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assert len(update_buffer["action"]) == 20
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assert np.array(update_buffer["action"]).shape == (20, 2)
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c = update_buffer.make_mini_batch(start=0, end=1)
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assert c.keys() == update_buffer.keys()
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assert np.array(c["action"]).shape == (1, 2)
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def fakerandint(values):
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return 19
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def test_buffer_sample():
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b = construct_fake_processing_buffer()
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update_buffer = AgentBuffer()
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b.append_to_update_buffer(update_buffer, 3, batch_size=None, training_length=2)
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b.append_to_update_buffer(update_buffer, 2, batch_size=None, training_length=2)
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# Test non-LSTM
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mb = update_buffer.sample_mini_batch(batch_size=4, sequence_length=1)
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assert mb.keys() == update_buffer.keys()
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assert np.array(mb["action"]).shape == (4, 2)
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# Test LSTM
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# We need to check if we ever get a breaking start - this will maximize the probability
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mb = update_buffer.sample_mini_batch(batch_size=20, sequence_length=19)
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assert mb.keys() == update_buffer.keys()
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# Should only return one sequence
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assert np.array(mb["action"]).shape == (19, 2)
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def test_buffer_truncate():
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b = construct_fake_processing_buffer()
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update_buffer = AgentBuffer()
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b.append_to_update_buffer(update_buffer, 3, batch_size=None, training_length=2)
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b.append_to_update_buffer(update_buffer, 2, batch_size=None, training_length=2)
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# Test non-LSTM
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update_buffer.truncate(2)
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assert len(update_buffer["action"]) == 2
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b.append_to_update_buffer(update_buffer, 3, batch_size=None, training_length=2)
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b.append_to_update_buffer(update_buffer, 2, batch_size=None, training_length=2)
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# Test LSTM, truncate should be some multiple of sequence_length
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update_buffer.truncate(4, sequence_length=3)
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assert len(update_buffer["action"]) == 3
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