updated test to pytest
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35
test/test_custom_envs.py
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35
test/test_custom_envs.py
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import fancy_gym
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import gym
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import pytest
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from test.utils import run_env, run_env_determinism
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CUSTOM_IDS = [spec.id for spec in gym.envs.registry.all() if
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"fancy_gym" in spec.entry_point and 'make_bb_env_helper' not in spec.entry_point]
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SEED = 1
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@pytest.mark.parametrize('env_id', CUSTOM_IDS)
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def test_step_functionality(env_id: str):
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"""Tests that step environments run without errors using random actions."""
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run_env(env_id)
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@pytest.mark.parametrize('env_id', CUSTOM_IDS)
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def test_step_determinism(env_id: str):
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"""Tests that for step environments identical seeds produce identical trajectories."""
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seed = 0
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run_env_determinism(env_id, seed)
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@pytest.mark.parametrize('env_id', fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
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def test_bb_functionality(env_id: str):
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"""Tests that black box environments run without errors using random actions."""
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run_env(env_id)
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@pytest.mark.parametrize('env_id', fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values())
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def test_bb_determinism(env_id: str):
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"""Tests that for black box environment identical seeds produce identical trajectories."""
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seed = 0
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run_env_determinism(env_id, seed)
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@ -1,42 +1,49 @@
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import numpy as np
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from typing import Tuple
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import fancy_gym
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import pytest
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from dm_control import suite, manipulation
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DMC_ENVS = [f'{env}-{task}' for env, task in suite.ALL_TASKS if env != "lqr"]
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MANIPULATION_SPECS = [f'manipulation-{task}' for task in manipulation.ALL if task.endswith('_features')]
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from test.utils import run_env_determinism, run_env
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SUITE_IDS = [f'dmc:{env}-{task}' for env, task in suite.ALL_TASKS if env != "lqr"]
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MANIPULATION_IDS = [f'dmc:manipulation-{task}' for task in manipulation.ALL if task.endswith('_features')]
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SEED = 1
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@pytest.mark.parametrize('env_id', DMC_ENVS)
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def test_dmc_functionality(self, env_id: str):
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"""Tests that environments runs without errors using random actions."""
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self.run_env(env_id)
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@pytest.mark.parametrize('env_id', SUITE_IDS)
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def test_step_suite_functionality(env_id: str):
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"""Tests that suite step environments run without errors using random actions."""
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run_env(env_id)
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@pytest.mark.parametrize('env_id', DMC_ENVS)
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def test_dmc_determinism(self, env_id: str):
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"""Tests that identical seeds produce identical trajectories."""
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@pytest.mark.parametrize('env_id', SUITE_IDS)
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def test_step_suite_determinism(env_id: str):
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"""Tests that for step environments identical seeds produce identical trajectories."""
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seed = 0
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self._run_env_determinism(env_id, seed)
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run_env_determinism(env_id, seed)
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@pytest.mark.parametrize('env_id', MANIPULATION_SPECS)
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def test_manipulation_functionality(self, env_id: str):
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"""Tests that environments runs without errors using random actions."""
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self.run_env(env_id)
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@pytest.mark.parametrize('env_id', MANIPULATION_IDS)
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def test_step_manipulation_functionality(env_id: str):
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"""Tests that manipulation step environments run without errors using random actions."""
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run_env(env_id)
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@pytest.mark.parametrize('env_id', MANIPULATION_SPECS)
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def test_manipulation_determinism(self, env_id: str):
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"""Tests that identical seeds produce identical trajectories."""
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@pytest.mark.parametrize('env_id', MANIPULATION_IDS)
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def test_step_manipulation_determinism(env_id: str):
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"""Tests that for step environments identical seeds produce identical trajectories."""
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seed = 0
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# Iterate over two trajectories, which should have the same state and action sequence
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traj1 = self.run_env(env_id, seed=seed)
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traj2 = self.run_env(env_id, seed=seed)
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for i, time_step in enumerate(zip(*traj1, *traj2)):
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obs1, rwd1, done1, obs2, rwd2, done2 = time_step
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assert np.array_equal(obs1, obs2), f"Observations [{i}] {obs1} and {obs2} do not match."
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assert np.all(rwd1 == rwd2), f"Rewards [{i}] {rwd1} and {rwd2} do not match."
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assert np.all(done1 == done2), f"Dones [{i}] {done1} and {done2} do not match."
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run_env_determinism(env_id, seed)
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@pytest.mark.parametrize('env_id', [fancy_gym.ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values()])
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def test_bb_dmc_functionality(env_id: str):
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"""Tests that black box environments run without errors using random actions."""
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run_env(env_id)
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@pytest.mark.parametrize('env_id', [fancy_gym.ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS.values()])
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def test_bb_dmc_determinism(env_id: str):
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"""Tests that for black box environment identical seeds produce identical trajectories."""
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run_env_determinism(env_id)
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@ -1,91 +0,0 @@
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import fancy_gym
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import gym
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import pytest
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from test.utils import run_env
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ALL_SPECS = list(spec for spec in gym.envs.registry.all() if "alr_envs" in spec.entry_point)
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SEED = 1
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@pytest.mark.parametrize('env_id', fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS['DMP'])
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def test_custom_dmp_functionality(env_id):
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"""Tests that environments runs without errors using random actions for custom DMP envs."""
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run_env(env_id)
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@pytest.mark.parametrize('env_id', fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS['ProMP'])
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def test_custom_promp_functionality(env_id):
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"""Tests that environments runs without errors using random actions for custom ProMP envs."""
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run_env(env_id)
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def test_openai_environment_functionality(self):
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"""Tests that environments runs without errors using random actions for OpenAI gym MP envs."""
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with self.subTest(msg="DMP"):
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for env_id in alr_envs.ALL_GYM_MOTION_PRIMITIVE_ENVIRONMENTS['DMP']:
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with self.subTest(msg=env_id):
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self.run_env(env_id)
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with self.subTest(msg="ProMP"):
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for env_id in alr_envs.ALL_GYM_MOTION_PRIMITIVE_ENVIRONMENTS['ProMP']:
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with self.subTest(msg=env_id):
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self.run_env(env_id)
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def test_dmc_environment_functionality(self):
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"""Tests that environments runs without errors using random actions for DMC MP envs."""
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with self.subTest(msg="DMP"):
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for env_id in alr_envs.ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS['DMP']:
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with self.subTest(msg=env_id):
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self.run_env(env_id)
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with self.subTest(msg="ProMP"):
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for env_id in alr_envs.ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS['ProMP']:
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with self.subTest(msg=env_id):
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self.run_env(env_id)
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def test_metaworld_environment_functionality(self):
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"""Tests that environments runs without errors using random actions for Metaworld MP envs."""
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with self.subTest(msg="DMP"):
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for env_id in alr_envs.ALL_METAWORLD_MOTION_PRIMITIVE_ENVIRONMENTS['DMP']:
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with self.subTest(msg=env_id):
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self.run_env(env_id)
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with self.subTest(msg="ProMP"):
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for env_id in alr_envs.ALL_METAWORLD_MOTION_PRIMITIVE_ENVIRONMENTS['ProMP']:
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with self.subTest(msg=env_id):
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self.run_env(env_id)
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def test_alr_environment_determinism(self):
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"""Tests that identical seeds produce identical trajectories for ALR MP Envs."""
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with self.subTest(msg="DMP"):
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self._run_env_determinism(alr_envs.ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["DMP"])
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with self.subTest(msg="ProMP"):
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self._run_env_determinism(alr_envs.ALL_ALR_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"])
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def test_openai_environment_determinism(self):
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"""Tests that identical seeds produce identical trajectories for OpenAI gym MP Envs."""
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with self.subTest(msg="DMP"):
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self._run_env_determinism(alr_envs.ALL_GYM_MOTION_PRIMITIVE_ENVIRONMENTS["DMP"])
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with self.subTest(msg="ProMP"):
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self._run_env_determinism(alr_envs.ALL_GYM_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"])
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def test_dmc_environment_determinism(self):
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"""Tests that identical seeds produce identical trajectories for DMC MP Envs."""
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with self.subTest(msg="DMP"):
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self._run_env_determinism(alr_envs.ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["DMP"])
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with self.subTest(msg="ProMP"):
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self._run_env_determinism(alr_envs.ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"])
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def test_metaworld_environment_determinism(self):
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"""Tests that identical seeds produce identical trajectories for Metaworld MP Envs."""
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with self.subTest(msg="DMP"):
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self._run_env_determinism(alr_envs.ALL_METAWORLD_MOTION_PRIMITIVE_ENVIRONMENTS["DMP"])
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with self.subTest(msg="ProMP"):
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self._run_env_determinism(alr_envs.ALL_METAWORLD_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"])
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@ -3,46 +3,43 @@ import numpy as np
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from fancy_gym import make
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def run_env(env_id, iterations=None, seed=SEED, render=False):
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def run_env(env_id, iterations=None, seed=0, render=False):
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"""
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Example for running a DMC based env in the step based setting.
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The env_id has to be specified as `domain_name-task_name` or
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The env_id has to be specified as `dmc:domain_name-task_name` or
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for manipulation tasks as `manipulation-environment_name`
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Args:
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env_id: Either `domain_name-task_name` or `manipulation-environment_name`
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env_id: Either `dmc:domain_name-task_name` or `dmc:manipulation-environment_name`
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iterations: Number of rollout steps to run
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seed= random seeding
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seed: random seeding
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render: Render the episode
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Returns:
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Returns: observations, rewards, dones, actions
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"""
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env: gym.Env = make(env_id, seed=seed)
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rewards = []
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observations = []
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actions = []
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dones = []
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obs = env.reset()
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_verify_observations(obs, env.observation_space, "reset()")
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verify_observations(obs, env.observation_space, "reset()")
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length = env.spec.max_episode_steps
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if iterations is None:
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if length is None:
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iterations = 1
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else:
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iterations = length
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iterations = iterations or (env.spec.max_episode_steps or 1)
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# number of samples(multiple environment steps)
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for i in range(iterations):
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observations.append(obs)
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ac = env.action_space.sample()
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actions.append(ac)
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# ac = np.random.uniform(env.action_space.low, env.action_space.high, env.action_space.shape)
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obs, reward, done, info = env.step(ac)
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_verify_observations(obs, env.observation_space, "step()")
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_verify_reward(reward)
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_verify_done(done)
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verify_observations(obs, env.observation_space, "step()")
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verify_reward(reward)
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verify_done(done)
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rewards.append(reward)
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dones.append(done)
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@ -51,34 +48,35 @@ def run_env(env_id, iterations=None, seed=SEED, render=False):
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env.render("human")
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if done:
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obs = env.reset()
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break
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assert done, "Done flag is not True after max episode length."
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assert done, "Done flag is not True after end of episode."
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observations.append(obs)
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env.close()
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del env
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return np.array(observations), np.array(rewards), np.array(dones)
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return np.array(observations), np.array(rewards), np.array(dones), np.array(actions)
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def _run_env_determinism(self, env_id: str, seed: int):
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traj1 = self.run_env(env_id, seed=seed)
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traj2 = self.run_env(env_id, seed=seed)
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def run_env_determinism(env_id: str, seed: int):
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traj1 = run_env(env_id, seed=seed)
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traj2 = run_env(env_id, seed=seed)
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# Iterate over two trajectories, which should have the same state and action sequence
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for i, time_step in enumerate(zip(*traj1, *traj2)):
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obs1, rwd1, done1, obs2, rwd2, done2 = time_step
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self.assertTrue(np.array_equal(obs1, obs2), f"Observations [{i}] {obs1} and {obs2} do not match.")
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self.assertEqual(rwd1, rwd2, f"Rewards [{i}] {rwd1} and {rwd2} do not match.")
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self.assertEqual(done1, done2, f"Dones [{i}] {done1} and {done2} do not match.")
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obs1, rwd1, done1, ac1, obs2, rwd2, done2, ac2 = time_step
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assert np.array_equal(obs1, obs2), f"Observations [{i}] {obs1} and {obs2} do not match."
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assert np.array_equal(ac1, ac2), f"Actions [{i}] {ac1} and {ac2} do not match."
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assert np.array_equal(rwd1, rwd2), f"Rewards [{i}] {rwd1} and {rwd2} do not match."
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assert np.array_equal(done1, done2), f"Dones [{i}] {done1} and {done2} do not match."
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def _verify_observations(obs, observation_space, obs_type="reset()"):
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def verify_observations(obs, observation_space, obs_type="reset()"):
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assert observation_space.contains(obs), \
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f"Observation {obs} received from {obs_type} not contained in observation space {observation_space}."
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def _verify_reward(reward):
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assert isinstance(reward, float), f"Returned {reward} as reward, expected float."
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def verify_reward(reward):
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assert isinstance(reward, (float, int)), f"Returned type {type(reward)} as reward, expected float or int."
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def _verify_done(done):
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def verify_done(done):
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assert isinstance(done, bool), f"Returned {done} as done flag, expected bool."
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