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 make_multiagent(env_name_or_creator):
deprecation_warning(
old="ray.rllib.examples.env.multi_agent.make_multiagent",
new="ray.rllib.env.multi_agent_env.make_multi_agent",
error=False,
)
return make_multi_agent(env_name_or_creator)
def test_space_in_preferred_format(self):
env = NestedMultiAgentEnv()
spaces_in_preferred_format = env._check_if_space_maps_agent_id_to_sub_space(
)
assert spaces_in_preferred_format, "Space is not in preferred format"
env2 = make_multi_agent("CartPole-v1")()
spaces_in_preferred_format = env2._check_if_space_maps_agent_id_to_sub_space(
)
assert (not spaces_in_preferred_format
), "Space should not be in preferred format but is."
def _make_base_env(self):
del self
num_envs = 2
sub_envs = [
make_multi_agent("CartPole-v1")({
"num_agents": 2
}) for _ in range(num_envs)
]
env = MultiAgentEnvWrapper(None, sub_envs, 2)
return 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_group_agents_wrapper(self):
MultiAgentCartPole = make_multi_agent("CartPole-v0")
grouped_ma_cartpole = GroupAgentsWrapper(
env=MultiAgentCartPole({"num_agents": 4}),
groups={"group1": [0, 1], "group2": [2, 3]},
)
obs = grouped_ma_cartpole.reset()
self.assertTrue(len(obs) == 2)
self.assertTrue("group1" in obs and "group2" in obs)
self.assertTrue(isinstance(obs["group1"], list) and len(obs["group1"]) == 2)
self.assertTrue(isinstance(obs["group2"], list) and len(obs["group2"]) == 2)
def test_spaces_sample_contain_not_in_preferred_format(self):
env = make_multi_agent("CartPole-v1")({"num_agents": 2})
# this environment has spaces that are not in the preferred format
# for multi-agent environments where the spaces not in the preferred
# format, users must override the observation_space_contains,
# action_space_contains observation_space_sample,
# and action_space_sample methods in order to do proper checks
obs = env.observation_space_sample()
assert env.observation_space_contains(
obs), "Observation space does not contain obs"
action = env.action_space_sample()
assert env.action_space_contains(
action), "Action space does not contain action"
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_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_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 make_multiagent(env_name_or_creator):
return make_multi_agent(env_name_or_creator)
obs_dict = {self.i: self.last_obs[self.i]}
self.i = (self.i + 1) % self.num
return obs_dict
def step(self, action_dict):
assert len(self.dones) != len(self.agents)
for i, action in action_dict.items():
(
self.last_obs[i],
self.last_rew[i],
self.last_done[i],
self.last_info[i],
) = self.agents[i].step(action)
obs = {self.i: self.last_obs[self.i]}
rew = {self.i: self.last_rew[self.i]}
done = {self.i: self.last_done[self.i]}
info = {self.i: self.last_info[self.i]}
if done[self.i]:
rew[self.i] = 0
self.dones.add(self.i)
self.i = (self.i + 1) % self.num
done["__all__"] = len(self.dones) == len(self.agents)
return obs, rew, done, info
MultiAgentCartPole = make_multi_agent("CartPole-v0")
MultiAgentMountainCar = make_multi_agent("MountainCarContinuous-v0")
MultiAgentPendulum = make_multi_agent("Pendulum-v1")
MultiAgentStatelessCartPole = make_multi_agent(
lambda config: StatelessCartPole(config))
Args:
mode (str): One of "rgb", "human", or "ascii". See gym.Env for
more information.
Returns:
Union[np.ndarray, bool]: An image to render or True (if rendering
is handled entirely in here).
"""
# Just generate a random image here for demonstration purposes.
# Also see `gym/envs/classic_control/cartpole.py` for
# an example on how to use a Viewer object.
return np.random.randint(0, 256, size=(300, 400, 3), dtype=np.uint8)
MultiAgentCustomRenderedEnv = make_multi_agent(
lambda config: CustomRenderedEnv(config))
if __name__ == "__main__":
# Note: Recording and rendering in this example
# should work for both local_mode=True|False.
ray.init(num_cpus=4)
args = parser.parse_args()
# Example config causing
config = {
# Also try common gym envs like: "CartPole-v0" or "Pendulum-v0".
"env": (MultiAgentCustomRenderedEnv
if args.multi_agent else CustomRenderedEnv),
"env_config": {
"corridor_length": 10,
"max_steps": 100,
