def _initialize(self):
"""Initialize non-common things."""
# load demo replay memory
with open(self.args.demo_path, "rb") as f:
demo = list(pickle.load(f))
# HER
if self.hyper_params.use_her:
self.her = build_her(self.hyper_params.her)
print(f"[INFO] Build {str(self.her)}.")
if self.hyper_params.desired_states_from_demo:
self.her.fetch_desired_states_from_demo(demo)
self.transitions_epi: list = list()
self.desired_state = np.zeros((1, ))
demo = self.her.generate_demo_transitions(demo)
if not self.her.is_goal_in_state:
self.state_dim = (self.state_dim[0] * 2, )
else:
self.her = None
if not self.args.test:
# Replay buffers
demo_batch_size = self.hyper_params.demo_batch_size
self.demo_memory = ReplayBuffer(len(demo), demo_batch_size)
self.demo_memory.extend(demo)
self.memory = ReplayBuffer(self.hyper_params.buffer_size,
demo_batch_size)
# set hyper parameters
self.lambda2 = 1.0 / demo_batch_size
def _initialize(self):
"""Initialize non-common things."""
# load demo replay memory
with open(self.args.demo_path, "rb") as f:
demo = list(pickle.load(f))
# HER
if self.hyper_params.use_her:
self.her = build_her(self.hyper_params.her)
print(f"[INFO] Build {str(self.her)}.")
if self.hyper_params.desired_states_from_demo:
self.her.fetch_desired_states_from_demo(demo)
self.transitions_epi: list = list()
self.desired_state = np.zeros((1, ))
demo = self.her.generate_demo_transitions(demo)
if not self.her.is_goal_in_state:
self.state_dim = (self.state_dim[0] * 2, )
else:
self.her = None
if not self.args.test:
# Replay buffers
demo_batch_size = self.hyper_params.demo_batch_size
self.demo_memory = ReplayBuffer(len(demo), demo_batch_size)
self.demo_memory.extend(demo)
self.memory = ReplayBuffer(self.hyper_params.sac_buffer_size,
demo_batch_size)
# set hyper parameters
self.hyper_params["lambda2"] = 1.0 / demo_batch_size
self.args.cfg_path = self.args.offer_cfg_path
self.args.load_from = self.args.load_offer_from
self.hyper_params.buffer_size = self.hyper_params.sac_buffer_size
self.hyper_params.batch_size = self.hyper_params.sac_batch_size
self.learner_cfg.type = "BCSACLearner"
self.learner_cfg.hyper_params = self.hyper_params
self.learner = build_learner(self.learner_cfg)
del self.hyper_params.buffer_size
del self.hyper_params.batch_size
# init stack
self.stack_size = self.args.stack_size
self.stack_buffer = deque(maxlen=self.args.stack_size)
self.stack_buffer_2 = deque(maxlen=self.args.stack_size)
self.scores = list()
self.utilities = list()
self.rounds = list()
self.opp_utilities = list()
def _initialize(self):
"""Initialize non-common things."""
# load demo replay memory
with open(self.hyper_params.demo_path, "rb") as f:
demo = list(pickle.load(f))
# HER
if self.hyper_params.use_her:
self.her = build_her(self.hyper_params.her)
print(f"[INFO] Build {str(self.her)}.")
if self.hyper_params.desired_states_from_demo:
self.her.fetch_desired_states_from_demo(demo)
self.transitions_epi: list = list()
self.desired_state = np.zeros((1, ))
demo = self.her.generate_demo_transitions(demo)
if not self.her.is_goal_in_state:
self.env_info.observation_space.shape = (
self.self.env_info.observation_space.shape[0] * 2, )
else:
self.her = None
if not self.is_test:
# Replay buffers
demo_batch_size = self.hyper_params.demo_batch_size
self.demo_memory = ReplayBuffer(len(demo), demo_batch_size)
self.demo_memory.extend(demo)
self.memory = ReplayBuffer(self.hyper_params.buffer_size,
self.hyper_params.batch_size)
# set hyper parameters
self.hyper_params["lambda2"] = 1.0 / demo_batch_size
build_args = dict(
hyper_params=self.hyper_params,
log_cfg=self.log_cfg,
noise_cfg=self.noise_cfg,
env_name=self.env_info.name,
state_size=self.env_info.observation_space.shape,
output_size=self.env_info.action_space.shape[0],
is_test=self.is_test,
load_from=self.load_from,
)
self.learner = build_learner(self.learner_cfg, build_args)