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util.py
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import torch
import os
import numpy as np
import gym
from gym import wrappers
from rlkit.envs.wrappers import NormalizedBoxEnv
from rlkit.torch.networks import FlattenMlp, TanhMlpPolicy
from rlkit.torch.sac.policies import TanhGaussianPolicy, MakeDeterministic
from rlkit.exploration_strategies.base import PolicyWrappedWithExplorationStrategy
from rlkit.exploration_strategies.gaussian_strategy import GaussianStrategy
from rlkit.torch.sac.sac import SACTrainer
from rlkit.torch.td3.td3 import TD3Trainer
def add_vision_noise(obs, epoch):
satulation = 100.
sdv = torch.tensor([3.440133806003181, 3.192113342496682, 1.727412865751099]) /100. #Vision SDV for arm
noise = torch.distributions.Normal(torch.tensor([0.0, 0.0, 0.0]), sdv).sample().cuda()
noise *= min(1., epoch/satulation)
obs[:,-3:] += noise
return obs
def add_joint_noise(obs):
sdv = torch.ones(obs.size(1))*0.03
# print("joint_sdv", sdv.size()
noise = torch.distributions.Normal(torch.zeros(sdv.size()), sdv).sample().cuda()
# print("noise", noise.size(), noise)
obs[:] += noise
# print("obs", obs)
return obs
def load_visionmodel(xml_path, model_path, visionmodel):
visionmodel_path = model_path + "floatinghook_pull/checkpoint.pth.tar"
if not os.path.isfile(visionmodel_path):
raise RuntimeError("=> no checkpoint found at '{}'" .format(visionmodel_path))
checkpoint = torch.load(visionmodel_path)
visionmodel.load_state_dict(checkpoint['state_dict'])
best_pred = checkpoint['best_pred']
print("=> loaded checkpoint '{}' (epoch {}), best pred {}".format(visionmodel_path, checkpoint['epoch'], best_pred))
return visionmodel
def prepare_env(env_name, visionmodel_path=None, **env_kwargs):
from gym.spaces import Box
if env_name.find('doorenv')>-1:
# print("expl_env")
expl_env = NormalizedBoxEnv(gym.make(env_name, **env_kwargs))
xml_path = expl_env._wrapped_env.xml_path
if env_kwargs['visionnet_input']:
print("using vision")
eval_env = None
if env_kwargs['unity']:
expl_env._wrapped_env.init()
else:
# print("no vision")
# print("eval_env")
eval_env = NormalizedBoxEnv(gym.make(env_name, **env_kwargs))
env_obj = expl_env._wrapped_env
expl_env.observation_space = Box(np.zeros(env_obj.nn*2+3), np.zeros(env_obj.nn*2+3), dtype=np.float32)
if eval_env:
eval_env.observation_space = Box(np.zeros(env_obj.nn*2+3), np.zeros(env_obj.nn*2+3), dtype=np.float32)
elif env_name.find("Fetch")>-1:
env = gym.make(env_name, reward_type='sparse')
env = wrappers.FlattenDictWrapper(env, dict_keys=['observation', 'desired_goal'])
expl_env = NormalizedBoxEnv(env)
eval_env = NormalizedBoxEnv(env)
env_obj = None
else:
expl_env = NormalizedBoxEnv(gym.make(env_name))
eval_env = NormalizedBoxEnv(gym.make(env_name))
env_obj = None
return expl_env, eval_env, env_obj
def prepare_trainer(algorithm, expl_env, obs_dim, action_dim, pretrained_policy_load, variant):
print("Preparing for {} trainer.".format(algorithm))
if algorithm == "SAC":
if not pretrained_policy_load:
M = variant['layer_size']
qf1 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M, M],
)
qf2 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M, M],
)
target_qf1 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M, M],
)
target_qf2 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
hidden_sizes=[M, M],
)
policy = TanhGaussianPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[M, M],
)
else:
snapshot = torch.load(pretrained_policy_load)
qf1 = snapshot['trainer/qf1']
qf2 = snapshot['trainer/qf2']
target_qf1 = snapshot['trainer/target_qf1']
target_qf2 = snapshot['trainer/target_qf2']
policy = snapshot['exploration/policy']
if variant['trainer_kwargs']['use_automatic_entropy_tuning']:
log_alpha = snapshot['trainer/log_alpha']
variant['trainer_kwargs']['log_alpha'] = log_alpha
alpha_optimizer = snapshot['trainer/alpha_optimizer']
variant['trainer_kwargs']['alpha_optimizer'] = alpha_optimizer
print("loaded the pretrained policy {}".format(pretrained_policy_load))
eval_policy = MakeDeterministic(policy)
expl_policy = policy
trainer = SACTrainer(
env=expl_env,
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
**variant['trainer_kwargs']
)
elif algorithm == "TD3":
if not pretrained_policy_load:
qf1 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs']
)
qf2 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs']
)
target_qf1 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs']
)
target_qf2 = FlattenMlp(
input_size=obs_dim + action_dim,
output_size=1,
**variant['qf_kwargs']
)
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs']
)
target_policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs']
)
es = GaussianStrategy(
action_space=expl_env.action_space,
max_sigma=0.1,
min_sigma=0.1, # Constant sigma
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
expl_policy = exploration_policy
eval_policy = policy
else:
pass
trainer = TD3Trainer(
policy=policy,
qf1=qf1,
qf2=qf2,
target_qf1=target_qf1,
target_qf2=target_qf2,
target_policy=target_policy,
**variant['trainer_kwargs']
)
return expl_policy, eval_policy, trainer