def choose_action_softmax(self, state, epsilon):
q_vals = self.q_vals(state)
tau = epsilon
q_vals = q_vals / tau
exp_q_vals = np.exp(q_vals)
weights = dict()
for idx, val in enumerate(exp_q_vals):
weights[idx] = val
action = weightedRandomChoice(weights)
return action
def choose_action_softmax(self, state, epsilon):
q_vals = self.q_vals(state)
tau = epsilon
q_vals = q_vals / tau
exp_q_vals = np.exp(q_vals)
weights = dict()
for idx, val in enumerate(exp_q_vals):
weights[idx] = val
action = weightedRandomChoice(weights)
return action
def choose_action_softmax(self, state, epsilon):
q_vals, compression_loss = self.q_vals_and_compression_loss(state)
compression_loss_component = compression_loss / self.max_compression_loss
tau = epsilon + compression_loss_component
q_vals = q_vals / tau
exp_q_vals = np.exp(q_vals)
weights = dict()
for idx, val in enumerate(exp_q_vals):
weights[idx] = val
action = weightedRandomChoice(weights)
return action
def choose_action_softmax(self, state, epsilon):
q_vals, compression_loss = self.q_vals_and_compression_loss(state)
compression_loss_component = compression_loss / self.max_compression_loss
tau = epsilon + compression_loss_component
q_vals = q_vals / tau
exp_q_vals = np.exp(q_vals)
weights = dict()
for idx, val in enumerate(exp_q_vals):
weights[idx] = val
action = weightedRandomChoice(weights)
return action
