def _decide_action(self, s, g):
with self.sess.as_default(), self.graph.as_default():
self._exp_action = self._enable_stoch_policy(
) and MathUtil.flip_coin(self.exp_params_curr.rate)
#print("_decide_action._exp_action=",self._exp_action)
a, logp = self._eval_actor(s, g, self._exp_action)
return a[0], logp[0]
def _decide_action(self, s, g):
with self.sess.as_default(), self.graph.as_default():
self._exp_action = self._enable_stoch_policy() and MathUtil.flip_coin(
self.exp_params_curr.rate)
#print("_decide_action._exp_action=",self._exp_action)
a, logp = self._eval_actor(s, g, self._exp_action)
return a[0], logp[0]
def _decide_action(self, s, g):
with self.sess.as_default(), self.graph.as_default():
self._exp_action = False
a = self._eval_actor(s, g)[0]
logp = 0
if self._enable_stoch_policy():
# epsilon-greedy
rand_action = MathUtil.flip_coin(self.exp_params_curr.rate)
if rand_action:
norm_exp_noise = np.random.randn(*a.shape)
norm_exp_noise *= self.exp_params_curr.noise
exp_noise = norm_exp_noise * self.a_norm.std
a += exp_noise
logp = self._calc_action_logp(norm_exp_noise)
self._exp_action = True
return a, logp
def _decide_action(self, s, g):
with self.sess.as_default(), self.graph.as_default():
self._exp_action = False
a = self._eval_actor(s, g)[0]
logp = 0
if self._enable_stoch_policy():
# epsilon-greedy
rand_action = MathUtil.flip_coin(self.exp_params_curr.rate)
if rand_action:
norm_exp_noise = np.random.randn(*a.shape)
norm_exp_noise *= self.exp_params_curr.noise
exp_noise = norm_exp_noise * self.a_norm.std
a += exp_noise
logp = self._calc_action_logp(norm_exp_noise)
self._exp_action = True
return a, logp