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57 行
1.8 KiB
57 行
1.8 KiB
import pytest
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import attr
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from mlagents.trainers.tests.simple_test_envs import (
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SimpleEnvironment,
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MemoryEnvironment,
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)
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from mlagents.trainers.settings import NetworkSettings, FrameworkType
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from mlagents.trainers.tests.dummy_config import ppo_dummy_config, sac_dummy_config
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from mlagents.trainers.tests.check_env_trains import check_environment_trains
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BRAIN_NAME = "1D"
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PPO_TORCH_CONFIG = attr.evolve(ppo_dummy_config(), framework=FrameworkType.PYTORCH)
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SAC_TORCH_CONFIG = attr.evolve(sac_dummy_config(), framework=FrameworkType.PYTORCH)
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def test_hybrid_ppo():
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env = SimpleEnvironment([BRAIN_NAME], action_sizes=(1, 1))
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config = attr.evolve(PPO_TORCH_CONFIG)
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check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=1.0)
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@pytest.mark.parametrize("num_visual", [1, 2])
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def test_visual_ppo(num_visual):
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env = SimpleEnvironment(
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[BRAIN_NAME], num_visual=num_visual, num_vector=0, action_sizes=(1, 1)
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)
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new_hyperparams = attr.evolve(
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PPO_TORCH_CONFIG.hyperparameters, learning_rate=3.0e-4
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)
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config = attr.evolve(PPO_TORCH_CONFIG, hyperparameters=new_hyperparams)
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check_environment_trains(env, {BRAIN_NAME: config})
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def test_recurrent_ppo():
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env = MemoryEnvironment([BRAIN_NAME], action_sizes=(1, 1))
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new_network_settings = attr.evolve(
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PPO_TORCH_CONFIG.network_settings,
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memory=NetworkSettings.MemorySettings(memory_size=16),
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)
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new_hyperparams = attr.evolve(
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PPO_TORCH_CONFIG.hyperparameters,
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learning_rate=1.0e-3,
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batch_size=64,
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buffer_size=128,
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)
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config = attr.evolve(
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PPO_TORCH_CONFIG,
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hyperparameters=new_hyperparams,
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network_settings=new_network_settings,
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max_steps=10000,
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)
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check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
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