def load(self, t=None):
"""Load."""
state_dict = self.ckptr.load(t)
if state_dict is None:
self.t = 0
return self.t
self.pi.load_state_dict(state_dict['pi'])
self.qf1.load_state_dict(state_dict['qf1'])
self.qf2.load_state_dict(state_dict['qf2'])
self.target_qf1.load_state_dict(state_dict['target_qf1'])
self.target_qf2.load_state_dict(state_dict['target_qf2'])
self.opt_pi.load_state_dict(state_dict['opt_pi'])
self.opt_qf1.load_state_dict(state_dict['opt_qf1'])
self.opt_qf2.load_state_dict(state_dict['opt_qf2'])
if state_dict['log_alpha']:
with torch.no_grad():
self.log_alpha.copy_(state_dict['log_alpha'])
self.opt_alpha.load_state_dict(state_dict['opt_alpha'])
misc.env_load_state_dict(self.env, state_dict['env'])
self.t = state_dict['t']
buffer_format = state_dict['buffer_format']
buffer_state = dict(
np.load(os.path.join(self.ckptr.ckptdir, 'buffer.npz'),
allow_pickle=True))
buffer_state = nest.flatten(buffer_state)
self.buffer.load_state_dict(
nest.pack_sequence_as(buffer_state, buffer_format))
self.data_manager.manual_reset()
return self.t
def from_tensors(self, tensors):
flat_dists = [
d.from_tensors(t)
for d, t in zip(nest.flatten(self.dists), tensors)
]
return ProductDistribution(
nest.pack_sequence_as(flat_dists, nest.get_structure(self.dists)))
def load(self, t=None):
"""Load state dict."""
state_dict = self.ckptr.load(t)
if state_dict is None:
self.t = 0
return self.t
self.pi.load_state_dict(state_dict['pi'])
self.opt.load_state_dict(state_dict['opt'])
self.t = state_dict['t']
buffer_format = state_dict['buffer_format']
buffer_state = dict(
np.load(os.path.join(self.ckptr.ckptdir, 'buffer.npz')))
buffer_state = nest.flatten(buffer_state)
self.buffer.load_state_dict(
nest.pack_sequence_as(buffer_state, buffer_format))
return self.t
def load(self, t=None):
"""Load."""
state_dict = self.ckptr.load(t)
if state_dict is None:
self.t = 0
return self.t
self.qf.load_state_dict(state_dict['qf'])
self.qf_targ.load_state_dict(state_dict['qf_targ'])
self.opt.load_state_dict(state_dict['opt'])
self._actor.load_state_dict(state_dict['_actor'])
misc.env_load_state_dict(self.env, state_dict['env'])
self.t = state_dict['t']
buffer_format = state_dict['buffer_format']
buffer_state = dict(
np.load(os.path.join(self.ckptr.ckptdir, 'buffer.npz')))
buffer_state = nest.flatten(buffer_state)
self.buffer.load_state_dict(
nest.pack_sequence_as(buffer_state, buffer_format))
self.data_manager.manual_reset()
return self.t
def load(self, t=None):
"""Load state dict."""
state_dict = self.ckptr.load(t)
if state_dict is None:
self.t = 0
return self.t
self.pi.load_state_dict(state_dict['pi'])
self.vf.load_state_dict(state_dict['vf'])
self.opt_pi.load_state_dict(state_dict['opt_pi'])
self.opt_vf.load_state_dict(state_dict['opt_vf'])
self.kl_weight = state_dict['kl_weight']
misc.env_load_state_dict(self.env, state_dict['env'])
self.ngu.load_state_dict(state_dict['ngu'])
self.t = state_dict['t']
buffer_format = state_dict['buffer_format']
buffer_state = dict(
np.load(os.path.join(self.ckptr.ckptdir, 'buffer.npz'),
allow_pickle=True))
buffer_state = nest.flatten(buffer_state)
self.buffer.load_state_dict(
nest.pack_sequence_as(buffer_state, buffer_format))
self.buffer.env_reset()
return self.t
