def test_check_env_step_incorrect_error(self):
step = MagicMock(return_value=(5, 5, True, {}))
env = make_multi_agent("CartPole-v1")({"num_agents": 2})
sampled_obs = env.reset()
env.step = step
with pytest.raises(ValueError, match="The element returned by step"):
check_env(env)
step = MagicMock(return_value=(sampled_obs, "Not a reward", True, {}))
env.step = step
with pytest.raises(AssertionError,
match="Your step function must "
"return a reward "):
check_env(env)
step = MagicMock(return_value=(sampled_obs, 5, "Not a bool", {}))
env.step = step
with pytest.raises(AssertionError,
match="Your step function must "
"return a done"):
check_env(env)
step = MagicMock(return_value=(sampled_obs, 5, False, "Not a Dict"))
env.step = step
with pytest.raises(AssertionError,
match="Your step function must "
"return a info"):
check_env(env)
def test_reset(self):
reset = MagicMock(return_value=5)
env = RandomEnv()
env.reset = reset
# check reset with out of bounds fails
error = ".*The observation collected from env.reset().*"
with pytest.raises(ValueError, match=error):
check_env(env)
# check reset with obs of incorrect type fails
reset = MagicMock(return_value=float(1))
env.reset = reset
with pytest.raises(ValueError, match=error):
check_env(env)
del env
def test_check_env_reset_incorrect_error(self):
reset = MagicMock(return_value=5)
env = make_multi_agent("CartPole-v1")({"num_agents": 2})
env.reset = reset
with pytest.raises(ValueError, match="The element returned by reset"):
check_env(env)
bad_obs = {
0: np.array([np.inf, np.inf, np.inf, np.inf]),
1: np.array([np.inf, np.inf, np.inf, np.inf]),
}
env.reset = lambda *_: bad_obs
with pytest.raises(ValueError,
match="The observation collected from env"):
check_env(env)
def test_check_space_contains_functions_errors(self):
def bad_contains_function(self, x):
raise ValueError("This is a bad contains function")
env = self._make_base_env()
env.observation_space_contains = bad_contains_function
with pytest.raises(
ValueError,
match="Your observation_space_contains function has some"):
check_env(env)
env = self._make_base_env()
env.action_space_contains = bad_contains_function
with pytest.raises(
ValueError,
match="Your action_space_contains function has some error"):
check_env(env)
def test_bad_sample_function(self):
env = make_multi_agent("CartPole-v1")({"num_agents": 2})
bad_action = {0: 2, 1: 2}
env.action_space_sample = lambda *_: bad_action
with pytest.raises(
ValueError,
match="The action collected from action_space_sample"):
check_env(env)
env = make_multi_agent("CartPole-v1")({"num_agents": 2})
bad_obs = {
0: np.array([np.inf, np.inf, np.inf, np.inf]),
1: np.array([np.inf, np.inf, np.inf, np.inf]),
}
env.observation_space_sample = lambda *_: bad_obs
with pytest.raises(
ValueError,
match="The observation collected from observation_space_sample",
):
check_env(env)
def test_bad_sample_function(self):
env = self._make_base_env()
bad_action = {0: {0: 2, 1: 2}}
env.action_space_sample = lambda *_: bad_action
with pytest.raises(
ValueError,
match="The action collected from action_space_sample"):
check_env(env)
env = self._make_base_env()
bad_obs = {
0: {
0: np.array([np.inf, np.inf, np.inf, np.inf]),
1: np.array([np.inf, np.inf, np.inf, np.inf]),
}
}
env.observation_space_sample = lambda *_: bad_obs
with pytest.raises(
ValueError,
match="The observation collected from observation_space_sample",
):
check_env(env)
def test_check_env_step_incorrect_error(self):
good_reward = {0: {0: 0, 1: 0}, 1: {0: 0, 1: 0}}
good_done = {0: {0: False, 1: False}, 1: {0: False, 1: False}}
good_info = {0: {0: {}, 1: {}}, 1: {0: {}, 1: {}}}
env = self._make_base_env()
bad_multi_env_dict_obs = {0: 1, 1: {0: np.zeros(4)}}
poll = MagicMock(return_value=(bad_multi_env_dict_obs, good_reward,
good_done, good_info, {}))
env.poll = poll
with pytest.raises(
ValueError,
match="The element returned by step, "
"next_obs has values that are not"
" MultiAgentDicts",
):
check_env(env)
bad_reward = {0: {0: "not_reward", 1: 1}}
good_obs = env.observation_space_sample()
poll = MagicMock(return_value=(good_obs, bad_reward, good_done,
good_info, {}))
env.poll = poll
with pytest.raises(AssertionError,
match="Your step function must "
"return a rewards that are"):
check_env(env)
bad_done = {0: {0: "not_done", 1: False}}
poll = MagicMock(return_value=(good_obs, good_reward, bad_done,
good_info, {}))
env.poll = poll
with pytest.raises(
AssertionError,
match="Your step function must "
"return a done that is "
"boolean.",
):
check_env(env)
bad_info = {0: {0: "not_info", 1: {}}}
poll = MagicMock(return_value=(good_obs, good_reward, good_done,
bad_info, {}))
env.poll = poll
with pytest.raises(
AssertionError,
match="Your step function must"
" return a info that is a "
"dict.",
):
check_env(env)
def test_step(self):
step = MagicMock(return_value=(5, 5, True, {}))
env = RandomEnv()
env.step = step
error = ".*The observation collected from env.step.*"
with pytest.raises(ValueError, match=error):
check_env(env)
# check reset that returns obs of incorrect type fails
step = MagicMock(return_value=(float(1), 5, True, {}))
env.step = step
with pytest.raises(ValueError, match=error):
check_env(env)
# check step that returns reward of non float/int fails
step = MagicMock(return_value=(1, "Not a valid reward", True, {}))
env.step = step
error = ("Your step function must return a reward that is integer or "
"float.")
with pytest.raises(AssertionError, match=error):
check_env(env)
# check step that returns a non bool fails
step = MagicMock(return_value=(1, float(5), "not a valid done signal",
{}))
env.step = step
error = "Your step function must return a done that is a boolean."
with pytest.raises(AssertionError, match=error):
check_env(env)
# check step that returns a non dict fails
step = MagicMock(return_value=(1, float(5), True,
"not a valid env info"))
env.step = step
error = "Your step function must return a info that is a dict."
with pytest.raises(AssertionError, match=error):
check_env(env)
del env
def test_check_env_reset_incorrect_error(self):
reset = MagicMock(return_value=5)
env = self._make_base_env()
env.try_reset = reset
with pytest.raises(ValueError,
match=("MultiEnvDict. Instead, it is of"
" type")):
check_env(env)
obs_with_bad_agent_ids = {
2: np.array([np.inf, np.inf, np.inf, np.inf]),
1: np.array([np.inf, np.inf, np.inf, np.inf]),
}
obs_with_bad_env_ids = {"bad_env_id": obs_with_bad_agent_ids}
reset = MagicMock(return_value=obs_with_bad_env_ids)
env.try_reset = reset
with pytest.raises(ValueError,
match="has dict keys that don't "
"correspond to"):
check_env(env)
reset = MagicMock(return_value={0: obs_with_bad_agent_ids})
env.try_reset = reset
with pytest.raises(
ValueError,
match="The element returned by "
"try_reset has agent_ids that are"
" not the names of the agents",
):
check_env(env)
out_of_bounds_obs = {
0: {
0: np.array([np.inf, np.inf, np.inf, np.inf]),
1: np.array([np.inf, np.inf, np.inf, np.inf]),
}
}
env.try_reset = lambda *_: out_of_bounds_obs
with pytest.raises(ValueError,
match="The observation collected from "
"try_reset"):
check_env(env)
del env
def test_check_env_step_incorrect_error(self):
step = MagicMock(return_value=(5, 5, True, {}))
env = make_multi_agent("CartPole-v1")({"num_agents": 2})
sampled_obs = env.reset()
env.step = step
with pytest.raises(ValueError, match="The element returned by step"):
check_env(env)
step = MagicMock(return_value=(sampled_obs, {
0: "Not a reward"
}, {
0: True
}, {}))
env.step = step
with pytest.raises(ValueError,
match="Your step function must return rewards"):
check_env(env)
step = MagicMock(return_value=(sampled_obs, {
0: 5
}, {
0: "Not a bool"
}, {}))
env.step = step
with pytest.raises(ValueError,
match="Your step function must return dones"):
check_env(env)
step = MagicMock(return_value=(sampled_obs, {
0: 5
}, {
0: False
}, {
0: "Not a Dict"
}))
env.step = step
with pytest.raises(ValueError,
match="Your step function must return infos"):
check_env(env)
def test_check_incorrect_space_contains_functions_error(self):
def bad_contains_function(self, x):
raise ValueError("This is a bad contains function")
env = make_multi_agent("CartPole-v1")({"num_agents": 2})
env.observation_space_contains = bad_contains_function
with pytest.raises(
ValueError,
match="Your observation_space_contains function has some"):
check_env(env)
env = make_multi_agent("CartPole-v1")({"num_agents": 2})
bad_action = {0: 2, 1: 2}
env.action_space_sample = lambda *_: bad_action
with pytest.raises(
ValueError,
match="The action collected from action_space_sample"):
check_env(env)
env.action_space_contains = bad_contains_function
with pytest.raises(
ValueError,
match="Your action_space_contains function has some error"):
check_env(env)
def test_check_correct_env(self):
env = self._make_base_env()
check_env(env)
env = gym.make("CartPole-v0")
env = convert_to_base_env(env)
check_env(env)
def test_check_correct_env(self):
env = self._make_base_env()
check_env(env)
