import gym import pybullet_envs from stable_baselines3.common.vec_env.vec_frame_stack import VecFrameStack from stable_baselines3.common.vec_env import DummyVecEnv import common.utils as utils utils.folder = "exp5" venv = DummyVecEnv([utils.make_env(template=utils.template(400), robot_body=400, wrapper=None)]) venv = VecFrameStack(venv, 4) obs = venv.reset() print(obs.shape)
gym_interface.make_env(
rank=i,
seed=common.seed,
wrappers=default_wrapper,
render=args.render,
robot_body=args.train_bodies[i % len(args.train_bodies)])
for i in range(args.num_venvs)
])
normalize_kwargs = {}
if args.vec_normalize:
normalize_kwargs["gamma"] = hyperparams["gamma"]
venv = VecNormalize(venv, **normalize_kwargs)
if args.stack_frames > 1:
venv = VecFrameStack(venv, args.stack_frames)
keys_remove = ["normalize", "n_envs", "n_timesteps", "policy"]
for key in keys_remove:
if key in hyperparams:
del hyperparams[key]
print("Making eval environments...")
all_callbacks = []
for test_body in args.test_bodies:
body_info = 0
eval_venv = DummyVecEnv([
gym_interface.make_env(rank=0,
seed=common.seed + 1,
wrappers=default_wrapper,
render=False,
from utils.wrappers import DepthWrapper
log_dir = "./data/reach_depth_sb_log"
save_path = "./data/reach_depth_sb"
best_save_path = "./data/reach_depth_sb_best"
os.makedirs(log_dir, exist_ok=True)
def env_fn():
return DepthWrapper(
TimeLimit(gym.make("PepperReachDepth-v0", gui=False, dense=True),
max_episode_steps=100))
env = VecFrameStack(DummyVecEnv([env_fn]), n_stack=8, channels_order="first")
eval_env = VecFrameStack(DummyVecEnv([env_fn]),
n_stack=8,
channels_order="first")
policy_kwargs = dict(
activation_fn=th.nn.ReLU,
net_arch=[64, 64, 64],
normalize_images=False,
features_extractor_class=StackCNN,
features_extractor_kwargs=dict(features_dim=16,
linear_dim=16,
n_channels=1),
)
