def pick_action(self, context):
# context contains all context_vectors for all arms
self.context = context
self.means = np.array([
self._compute_mean(self.theta_hat[i], context[i])
for i in range(self.k_arms)
])
return random_argmax(self.means)
def observe_reward(self, arm_idx, reward):
self.arms_data[arm_idx].append(reward)
n = len(self.arms_data[arm_idx])
self.estimated_means[arm_idx] = reward / n + (
n - 1) * self.estimated_means[arm_idx] / n
self.pulls[arm_idx] += 1
self.best_arm = random_argmax(self.estimated_means)
self._t += 1
def pick_action(self):
# try each arm once then compute UCBs
if self._t > self.k_arms:
self._UCBs = [
self._bound_function(arm_idx) for arm_idx in self._arm_idxs
]
arm_idx = random_argmax(self._UCBs)
else:
arm_idx = next(self._try_each_arm)
return arm_idx
def pick_action(self):
# try each arm once then compute UCBs
if self._t > self.k_arms:
self.Vs = self.sq_sums - self.estimated_means**2 + self.radius(
self._t, self.pulls)
self._UCBs = self.estimated_means + np.sqrt(
(np.log(self._t) / self.pulls) * self.Vs)
arm_idx = random_argmax(self._UCBs)
else:
arm_idx = next(self._try_each_arm)
return arm_idx
def pick_action(self):
# try each arm once then compute UCBs
if self._t > self.k_arms:
self._UCBs = self.estimated_means + self.radius(
self._t, self.pulls)
arm_idx = random_argmax(self._UCBs)
if self.keep_history:
self.UCB_history.append(self._UCBs.copy())
self.means_history.append(self.estimated_means.copy())
self.pulls_history.append(self.pulls.copy())
else:
arm_idx = next(self._try_each_arm)
return arm_idx
def generate_context(self):
"""Generates context vector of indicators and computes current
real reward probability
"""
context = []
context_vector = bernuolli(self.context_options)
if self.add_bias:
context_vector = np.append([1], context_vector)
for i in range(self.k_arms):
context.append(context_vector)
# pull all arms to generate current mean, rewards and get the optimal one
# agent/policy/algorithm knows only the context but means are not revealed
self.current_rewards = [
arm.pull(context_vector)
for arm, context_vector in zip(self.arms, context)
]
self.current_means = [arm.get_current_mean() for arm in self.arms]
self.current_optimal_arm = random_argmax(self.current_means)
self.current_optimal_mean = self.current_means[
self.current_optimal_arm]
return context
def pick_action(self):
if self.keep_history:
self.prior_data_history.append(self.prior_data.copy())
self.means_history.append(self.estimated_means.copy())
self.pulls_history.append(self.pulls.copy())
return random_argmax(self._sample_from_arms())
