class_docstr: UCB exploration policy is following the upper confidence bound heuristic to sample actions in discrete action spaces.
  It assumes that there are multiple network heads that are predicting action values, and that the standard deviation
  between the heads predictions represents the uncertainty of the agent in each of the actions.
  It then updates the action value estimates to by mean(actions)+lambda*stdev(actions), where lambda is
  given by the user. This exploration policy aims to take advantage of the uncertainty of the agent in its predictions,
  and select the action according to the tradeoff between how uncertain the agent is, and how large it predicts
  the outcome from those actions to be.
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code: def select_head(self):
      pass
  
file_path: coach/rl_coach/exploration_policies/ucb.py
imports: from typing import List
  import numpy
  from rl_coach.core_types import RunPhase, ActionType, EnvironmentSteps
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.exploration_policies.e_greedy import EGreedy, EGreedyParameters
  from rl_coach.exploration_policies.exploration_policy import ExplorationParameters
  from rl_coach.schedules import Schedule, LinearSchedule, PieceWiseSchedule
  from rl_coach.spaces import ActionSpace
label_desc: Use image node_id 2 for calling the UCB obj's underlying member method code with example usage: obj.select_head() without return types