class_docstr: e-greedy is an exploration policy that is intended for both discrete and continuous action spaces.
  
  For discrete action spaces, it assumes that each action is assigned a value, and it selects the action with the
  highest value with probability 1 - epsilon. Otherwise, it selects a action sampled uniformly out of all the
  possible actions. The epsilon value is given by the user and can be given as a schedule.
  In evaluation, a different epsilon value can be specified.
  
  For continuous action spaces, it assumes that the mean action is given by the agent. With probability epsilon,
  it samples a random action out of the action space bounds. Otherwise, it selects the action according to a
  given continuous exploration policy, which is set to AdditiveNoise by default. In evaluation, the action is
  always selected according to the given continuous exploration policy (where its phase is set to evaluation as well).
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code: def step_epsilon(self):
      # step the epsilon schedule and generate a new random value for next time
      if self.phase == RunPhase.TRAIN:
          self.epsilon_schedule.step()
      self.current_random_value = np.random.rand()
  
file_path: coach/rl_coach/exploration_policies/e_greedy.py
imports: from typing import List
  import numpy
  from rl_coach.core_types import RunPhase, ActionType
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.exploration_policies.exploration_policy import ExplorationParameters, ExplorationPolicy
  from rl_coach.schedules import Schedule, LinearSchedule
  from rl_coach.spaces import ActionSpace, DiscreteActionSpace, BoxActionSpace
  from rl_coach.utils import dynamic_import_and_instantiate_module_from_params
label_desc: Use image node_id 6 for calling the EGreedy obj's underlying member method code with example usage: obj.step_epsilon() without return types