def make_eval_env(all_args):
def get_env_fn(rank):
def init_env():
if all_args.env_name == "MPE":
env = MPEEnv(all_args)
else:
print("Can not support the " +
all_args.env_name + "environment.")
raise NotImplementedError
env.seed(all_args.seed * 50000 + rank * 10000)
return env
return init_env
if all_args.n_eval_rollout_threads == 1:
return DummyVecEnv([get_env_fn(0)])
else:
return SubprocVecEnv([get_env_fn(i) for i in range(all_args.n_eval_rollout_threads)])
def make_train_env(all_args):
"""
the wrapper to instantiate the Highway env with multiple vehicles controlled by trained agents, Value Iteration based RL agent, training agent and rule-based agents (Intelligent Driver Model, IDM model).
"""
def get_env_fn(rank):
def init_env():
if all_args.env_name == "Highway":
env = HighwayEnv(all_args)
else:
print("Can not support the " +
all_args.env_name + "environment.")
raise NotImplementedError
env.seed(all_args.seed + rank * 1000)
return env
return init_env
if all_args.n_rollout_threads == 1:
return DummyVecEnv([get_env_fn(0)])
else:
return SubprocVecEnv([get_env_fn(i) for i in range(all_args.n_rollout_threads)])
def make_train_env(all_args):
def get_env_fn(rank):
def init_env():
if all_args.env_name == "Harvest":
env = HarvestEnv(all_args)
elif all_args.env_name == "Cleanup":
env = CleanupEnv(all_args)
else:
print("Can not support the " + all_args.env_name +
"environment.")
raise NotImplementedError
env.seed(all_args.seed + rank * 1000)
return env
return init_env
if all_args.n_rollout_threads == 1:
return DummyVecEnv([get_env_fn(0)])
else:
return SubprocVecEnv(
[get_env_fn(i) for i in range(all_args.n_rollout_threads)])
def make_train_env(all_args):
def get_env_fn(rank):
def init_env():
if True:
agent_spec = AgentSpec(
interface=AgentInterface.from_type(
AgentType.Laner, max_episode_steps=all_args.episode_length
)
)
AGENT_ID = [str(i) for i in range(all_args.num_agents)]
env = gym.make(
"smarts.env:hiway-v0",
scenarios=all_args.scenarios,
agent_specs={i: agent_spec for i in AGENT_ID},
headless=all_args.headless,
visdom=False,
timestep_sec=0.1,
sumo_headless=True,
seed=all_args.seed + rank * 1000,
# zoo_workers=[("143.110.210.157", 7432)], # Distribute social agents across these workers
auth_key=all_args.auth_key,
# envision_record_data_replay_path="./data_replay",
)
env = SmartWrapper(env, all_args.num_agents)
else:
print("Can not support the " +
all_args.env_name + "environment.")
raise NotImplementedError
# env.seed(all_args.seed + rank * 1000)
return env
return init_env
if all_args.n_rollout_threads == 1:
return DummyVecEnv([get_env_fn(0)])
else:
return SubprocVecEnv([get_env_fn(i) for i in range(all_args.n_rollout_threads)])
if __name__ == "__main__":
from onpolicy.config import get_config
from onpolicy.envs.gfootball.gfootball_env import GoogleFootballEnv
from onpolicy.envs.env_wrappers import SubprocVecEnv
import gym
gym.logger.set_level(gym.logger.ERROR)
args = get_config().parse_known_args()[0]
config = {
'all_args':
args,
'envs':
SubprocVecEnv([
lambda: GoogleFootballEnv(num_of_left_agents=3,
env_name='test_example_multiagent',
representation="simple115v2",
channel_dimensions=(48, 36))
for i in range(args.n_rollout_threads)
]),
'eval_envs':
SubprocVecEnv([
lambda: GoogleFootballEnv(num_of_left_agents=3,
env_name='test_example_multiagent',
representation="simple115v2",
channel_dimensions=(48, 36))
for i in range(args.n_eval_rollout_threads)
]),
'device':
None,
'num_agents':
3,