def _ivariants_sorted(self, sorted_keys):
if len(sorted_keys) == 0:
yield dict()
else:
first_keys = sorted_keys[:-1]
first_variants = self._ivariants_sorted(first_keys)
last_key = sorted_keys[-1]
last_vals = [v for k, v, _ in self._variants if k == last_key][0]
if hasattr(last_vals, "__call__"):
last_val_keys = inspect.getargspec(last_vals).args
if hasattr(last_vals, 'im_self') or hasattr(
last_vals, '__self__'):
last_val_keys = last_val_keys[1:]
else:
last_val_keys = None
for variant in first_variants:
if hasattr(last_vals, "__call__"):
last_variants = last_vals(
**{k: variant[k]
for k in last_val_keys})
for last_choice in last_variants:
yield AttrDict(variant, **{last_key: last_choice})
else:
for last_choice in last_vals:
yield AttrDict(variant, **{last_key: last_choice})
return samples
def get_action(self, t, observation, policy, **kwargs):
#applying MC Dropout and taking the mean action?
action, _ = policy.get_action(observation)
mc_dropout = 10
all_actions = np.zeros(shape=(mc_dropout, action.shape[0]))
for d in range(mc_dropout):
action, _ = policy.get_action(observation)
all_actions[d, :] = action
mean_action = np.mean(all_actions, axis=0)
return mean_action
if __name__ == "__main__":
ou = MCDropout(
env_spec=AttrDict(action_space=Box(low=-1, high=1, shape=(1, ))),
mu=0,
theta=0.15,
sigma=0.3)
states = []
for i in range(1000):
states.append(ou.evolve_state()[0])
import matplotlib.pyplot as plt
plt.plot(states)
plt.show()