def __init__(self, env_name, discount, num_iterations, lamb, animate,
kl_target):
self.env = gym.make(env_name)
gym.spaces.seed(1234)
self.obs_dim = self.env.observation_space.shape[0] + 1
self.act_dim = self.env.action_space.shape[0]
self.discount = discount
self.num_iterations = num_iterations
self.lamb = lamb
self.animate = animate
self.killer = GracefulKiller()
self.policy = ProximalPolicy(self.obs_dim,
self.act_dim,
self.env.action_space,
kl_target,
discount=discount,
lamb=lamb)
# using MC return would be more helpful
self.value_func = l2TargetValueFunc(self.obs_dim)
# self.value_func = ValueFunc(self.obs_dim, discount=discount, lamb=1)
# save copies of file
shutil.copy(inspect.getfile(self.policy.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.value_func.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.__class__), OUTPATH)
self.log_file = open(OUTPATH + 'log.csv', 'w')
self.write_header = True
print('observation dimension:', self.obs_dim)
print('action dimension:', self.act_dim)
self.scaler = Scaler(self.obs_dim)
self.init_scaler()
def __init__(self, discount, num_iterations, lamb, animate, kl_target, **kwargs):
self.env_name = 'RoboschoolHumanoidFlagrun-v1'
self.env = gym.make(self.env_name)
gym.spaces.seed(1234) # for reproducibility
self.obs_dim = self.env.observation_space.shape[0] + 1 # adding time step as feature
self.act_dim = self.env.action_space.shape[0]
self.discount = discount
self.num_iterations = num_iterations
self.lamb = lamb
self.animate = animate
self.buffer = Buffer(1000000, self.obs_dim, self.act_dim) # 1000000 is the size they have used in paper
self.episodes = 20 # larger episodes can reduce variance
self.killer = GracefulKiller()
self.policy = QPropPolicy(self.obs_dim, self.act_dim, self.env.action_space, kl_target, epochs=20)
self.critic = DeterministicCritic(self.obs_dim, self.act_dim, self.discount, OUTPATH)
self.value_func = l2TargetValueFunc(self.obs_dim, epochs=10)
if 'show' in kwargs and not kwargs['show']:
# save copies of file
shutil.copy(inspect.getfile(self.policy.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.value_func.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.critic.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.__class__), OUTPATH)
self.log_file = open(OUTPATH + 'log.csv', 'w')
self.write_header = True
print('Observation dimension:', self.obs_dim)
print('Action dimension:', self.act_dim)
# The use of a scaler is crucial
self.scaler = Scaler(self.obs_dim)
self.init_scaler()
def __init__(self, env_name, discount, num_iterations, lamb, animate,
kl_target, show):
self.env_name = env_name
self.env = gym.make(env_name)
if env_name == "FetchReach-v0":
self.env = gym.wrappers.FlattenDictWrapper(
self.env, ['observation', 'desired_goal', 'achieved_goal'])
gym.spaces.seed(1234)
self.obs_dim = self.env.observation_space.shape[
0] + 1 # adding time step as feature
self.act_dim = self.env.action_space.shape[0]
self.discount = discount
self.num_iterations = num_iterations
self.lamb = lamb
self.animate = animate
self.buffer = Buffer(1000000, self.obs_dim, self.act_dim)
self.episodes = 20
self.killer = GracefulKiller()
self.policy = QPropPolicy(self.obs_dim,
self.act_dim,
self.env.action_space,
kl_target,
epochs=20)
self.critic = DeterministicCritic(self.obs_dim, self.act_dim,
self.discount, OUTPATH)
# using MC return would be more helpful
self.value_func = l2TargetValueFunc(self.obs_dim, epochs=10)
if not show:
# save copies of file
shutil.copy(inspect.getfile(self.policy.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.value_func.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.critic.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.__class__), OUTPATH)
self.log_file = open(OUTPATH + 'log.csv', 'w')
self.write_header = True
print('observation dimension:', self.obs_dim)
print('action dimension:', self.act_dim)
# Use of a scaler is crucial
self.scaler = Scaler(self.obs_dim)
self.init_scaler()
