707d8f6189
- Added check in access_control_list.py which sets implicit permission to NA if boolean is False - Changed the defaults in training_config.py so that each scenario has an EXPLICIT ALLOW rule as default implicit rule - Updated the test_seeding_and_deterministic_session.py because of change no2 adds an extra rule to that scenario
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
import pytest as pytest
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from primaite.config.lay_down_config import dos_very_basic_config_path
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from tests import TEST_CONFIG_ROOT
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@pytest.mark.parametrize(
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"temp_primaite_session",
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[[TEST_CONFIG_ROOT / "ppo_seeded_training_config.yaml", dos_very_basic_config_path()]],
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indirect=True,
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)
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def test_seeded_learning(temp_primaite_session):
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"""
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Test running seeded learning produces the same output when ran twice.
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.. note::
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If this is failing, the hard-coded expected_mean_reward_per_episode
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from a pre-trained agent will probably need to be updated. If the
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env changes and those changed how this agent is trained, chances are
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the mean rewards are going to be different.
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Run the test, but print out the session.learn_av_reward_per_episode()
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before comparing it. Then copy the printed dict and replace the
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expected_mean_reward_per_episode with those values. The test should
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now work. If not, then you've got a bug :).
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"""
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expected_mean_reward_per_episode = {
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1: -90.703125,
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2: -91.15234375,
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3: -87.5,
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4: -92.2265625,
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5: -94.6875,
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6: -91.19140625,
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7: -88.984375,
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8: -88.3203125,
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9: -112.79296875,
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10: -100.01953125,
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}
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with temp_primaite_session as session:
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assert (
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session._training_config.seed == 67890
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), "Expected output is based upon a agent that was trained with seed 67890"
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session.learn()
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print("\n")
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print(session.learn_av_reward_per_episode())
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assert expected_mean_reward_per_episode == session.learn_av_reward_per_episode()
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@pytest.mark.skip(reason="Inconsistent results. Needs someone with RL knowledge to investigate further.")
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@pytest.mark.parametrize(
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"temp_primaite_session",
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[[TEST_CONFIG_ROOT / "ppo_seeded_training_config.yaml", dos_very_basic_config_path()]],
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indirect=True,
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)
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def test_deterministic_evaluation(temp_primaite_session):
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"""Test running deterministic evaluation gives same av eward per episode."""
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with temp_primaite_session as session:
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# do stuff
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session.learn()
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session.evaluate()
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eval_mean_reward = session.eval_av_reward_per_episode_csv()
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assert len(set(eval_mean_reward.values())) == 1
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