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61 行
1.7 KiB
61 行
1.7 KiB
import numpy as np
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import pytest
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from mlagents.trainers.trajectory import SplitObservations
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from mlagents.trainers.tests.mock_brain import make_fake_trajectory
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from mlagents_envs.base_env import ActionSpec
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VEC_OBS_SIZE = 6
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ACTION_SIZE = 4
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@pytest.mark.parametrize("num_visual_obs", [0, 1, 2])
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@pytest.mark.parametrize("num_vec_obs", [0, 1])
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def test_split_obs(num_visual_obs, num_vec_obs):
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obs = []
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for _ in range(num_visual_obs):
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obs.append(np.ones((84, 84, 3), dtype=np.float32))
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for _ in range(num_vec_obs):
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obs.append(np.ones(VEC_OBS_SIZE, dtype=np.float32))
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split_observations = SplitObservations.from_observations(obs)
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if num_vec_obs == 1:
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assert len(split_observations.vector_observations) == VEC_OBS_SIZE
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else:
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assert len(split_observations.vector_observations) == 0
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# Assert the number of vector observations.
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assert len(split_observations.visual_observations) == num_visual_obs
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def test_trajectory_to_agentbuffer():
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length = 15
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wanted_keys = [
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"next_visual_obs0",
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"visual_obs0",
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"vector_obs",
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"next_vector_in",
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"memory",
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"masks",
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"done",
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"actions_pre",
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"continuous_action",
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"action_probs",
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"action_mask",
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"prev_continuous_action",
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"environment_rewards",
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]
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wanted_keys = set(wanted_keys)
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trajectory = make_fake_trajectory(
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length=length,
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observation_shapes=[(VEC_OBS_SIZE,), (84, 84, 3)],
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action_spec=ActionSpec.create_continuous(ACTION_SIZE),
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)
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agentbuffer = trajectory.to_agentbuffer()
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seen_keys = set()
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for key, field in agentbuffer.items():
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assert len(field) == length
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seen_keys.add(key)
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assert seen_keys == wanted_keys
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