code: def act_e_greedy(self, state, epsilon=0.001):
      return (
          random.randrange(self.action_space)
          if random.random() < epsilon
          else self.act(state)
      )
  
file_path: SerpentAI/serpent/machine_learning/reinforcement_learning/rainbow_dqn/rainbow_agent.py
imports: import os
  import random
  import torch
  from torch import nn, optim
  from serpent.machine_learning.reinforcement_learning.rainbow_dqn.dqn import DQN3, DQN4
label_desc: Use image node_id 4 for calling the RainbowAgent obj's underlying member method code with example usage: obj.act_e_greedy(state, epsilon) and returns: unknown