def test_multiagent_mutliproc_single_proc_equivalency():
env = simple_spread_v2.parallel_env()
env = pettingzoo_env_to_vec_env_v0(env)
num_envs = 3
venv1 = concat_vec_envs_v0(
env, num_envs, num_cpus=0) # uses single threaded vector environment
venv2 = concat_vec_envs_v0(
env, num_envs, num_cpus=4) # uses multiprocessing vector environment
check_vec_env_equivalency(venv1, venv2)
def env_fn():
#env = gym.make("CartPole-v0")#
env = simple_spread_v2.parallel_env()
# print(env.action_spaces.values())
# exit(0)
env = supersuit.pad_observations_v0(env)
env = supersuit.pad_action_space_v0(env)
#env = supersuit.aec_wrappers.continuous_actions(env)
venv = pettingzoo_env_to_vec_env_v0(env)
return venv
writer = SummaryWriter(f"/tmp/{experiment_name}")
# TRY NOT TO MODIFY: seeding
device = torch.device(
'cuda' if torch.cuda.is_available() and args.cuda else 'cpu')
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = args.torch_deterministic
# petting zoo
env = pistonball_v4.parallel_env()
env = ss.color_reduction_v0(env, mode='B')
env = ss.resize_v0(env, x_size=84, y_size=84)
env = ss.frame_stack_v1(env, 3)
env = ss.pettingzoo_env_to_vec_env_v0(env)
envs = ss.concat_vec_envs_v0(env,
args.num_envs,
num_cpus=0,
base_class='stable_baselines3')
envs = VecMonitor(envs)
if args.capture_video:
envs = VecVideoRecorder(envs,
f'videos/{experiment_name}',
record_video_trigger=lambda x: x % 150000 == 0,
video_length=400)
envs = VecPyTorch(envs, device)
args.num_envs = envs.num_envs
args.batch_size = int(args.num_envs * args.num_steps)
args.minibatch_size = int(args.batch_size // args.n_minibatch)
assert isinstance(envs.action_space,
def test_env_is_wrapped_pettingzoo():
env = simple_spread_v2.parallel_env()
venv1 = pettingzoo_env_to_vec_env_v0(env)
num_envs = 3
venv1 = concat_vec_envs_v0(venv1, num_envs)
assert venv1.env_is_wrapped(flatten) == [False] * 9
def make_env():
env = pistonball_v4.parallel_env()
env = pettingzoo_env_to_vec_env_v0(env)
return env