def get_action(self, obs):
act_dist = softmax(self.means / self.softmax_temp)
act = np.random.choice(range(self.env.nA), p=act_dist)
return act
def likelihood(self, state, obs, act):
""" Computes the likelihood of taking the action given the state. """
act_dist = softmax(state['means'] / state['temp'])
return act_dist[act]
def act_probs_from_counts(self, counts, **kwargs):
means = np.array([arm.mean() for arm in self.env.arms])
act_dist = softmax(means / self.softmax_temp)
return act_dist
def likelihood(self, state, obs, act):
arm_vals = state['means'] + state['ucl_bonus']
act_dist = softmax(arm_vals / state['softmax_temp'])
return act_dist[act]
def get_action_from_state(self, state, obs):
act_dist = softmax(state['means'] / state['temp'])
act = np.random.choice(range(self.env.nA), p=act_dist)
return act
def get_action_from_state(self, state, obs):
arm_vals = state['means'] + state['ucl_bonus']
act_dist = softmax(arm_vals / state['softmax_temp'])
act = self.np_random.choice(range(self.env.nA), p=act_dist)
return act
def get_action(self, obs):
self.arm_vals = self.means + self.ucl_bonus
act_dist = softmax(self.arm_vals / self.softmax_temp)
act = self.np_random.choice(range(self.env.nA), p=act_dist)
return act
def likelihood(self, state, obs, act):
""" Computes the likelihood of taking the action given the state. """
return softmax(state['Q_human'][obs] / self.softmax_temp)[act]
def get_action_from_state(self, state, obs):
act_dist = softmax(state['Q_human'][obs] / self.softmax_temp)
act = np.random.choice(range(self.env.nA), p=act_dist)
return act
def get_action(self, obs):
act_dist = softmax(self.Q_human[obs] / self.softmax_temp)
act = self.np_random.choice(range(self.env.nA), p=act_dist)
return act