code: def actor_learner_thread(
      thread_id,
      env,
      session,
      graph_ops,
      num_actions,
      summary_ops,
      saver,
  ):
      """
      Actor-learner thread implementing asynchronous one-step Q-learning, as specified
      in algorithm 1 here: http://arxiv.org/pdf/1602.01783v1.pdf.
      """
      global TMAX, T
  
      # Unpack graph ops
      s = graph_ops["s"]
      q_values = graph_ops["q_values"]
      st = graph_ops["st"]
      target_q_values = graph_ops["target_q_values"]
      reset_target_network_params = graph_ops[
          "reset_target_network_params"
      ]
      a = graph_ops["a"]
      y = graph_ops["y"]
      grad_update = graph_ops["grad_update"]
  
      summary_placeholders, assign_ops, summary_op = summary_ops
  
      # Wrap env with AtariEnvironment helper class
      env = AtariEnvironment(gym_env=env, action_repeat=action_repeat)
  
      # Initialize network gradients
      s_batch = []
      a_batch = []
      y_batch = []
  
      final_epsilon = sample_final_epsilon()
      initial_epsilon = 1.0
      epsilon = 1.0
  
      print(
          "Thread "
          + str(thread_id)
          + " - Final epsilon: "
          + str(final_epsilon)
      )
  
      time.sleep(3 * thread_id)
      t = 0
      while T < TMAX:
          # Get initial game observation
          s_t = env.get_initial_state()
          terminal = False
  
          # Set up per-episode counters
          ep_reward = 0
          episode_ave_max_q = 0
          ep_t = 0
  
          while True:
              # Forward the deep q network, get Q(s,a) values
              readout_t = q_values.eval(
                  session=session, feed_dict={s: [s_t]}
              )
  
              # Choose next action based on e-greedy policy
              a_t = np.zeros([num_actions])
              if random.random() <= epsilon:
                  action_index = random.randrange(num_actions)
              else:
                  action_index = np.argmax(readout_t)
              a_t[action_index] = 1
  
              # Scale down epsilon
              if epsilon > final_epsilon:
                  epsilon -= (
                      initial_epsilon - final_epsilon
                  ) / anneal_epsilon_timesteps
  
              # Gym excecutes action in game environment on behalf of actor-learner
              s_t1, r_t, terminal, info = env.step(action_index)
  
              # Accumulate gradients
              readout_j1 = target_q_values.eval(
                  session=session, feed_dict={st: [s_t1]}
              )
              clipped_r_t = np.clip(r_t, -1, 1)
              if terminal:
                  y_batch.append(clipped_r_t)
              else:
                  y_batch.append(
                      clipped_r_t + gamma * np.max(readout_j1)
                  )
  
              a_batch.append(a_t)
              s_batch.append(s_t)
  
              # Update the state and counters
              s_t = s_t1
              T += 1
              t += 1
  
              ep_t += 1
              ep_reward += r_t
              episode_ave_max_q += np.max(readout_t)
  
              # Optionally update target network
              if T % I_target == 0:
                  session.run(reset_target_network_params)
  
              # Optionally update online network
              if t % I_AsyncUpdate == 0 or terminal:
                  if s_batch:
                      session.run(
                          grad_update,
                          feed_dict={
                              y: y_batch,
                              a: a_batch,
                              s: s_batch,
                          },
                      )
                  # Clear gradients
                  s_batch = []
                  a_batch = []
                  y_batch = []
  
              # Save model progress
              if t % checkpoint_interval == 0:
                  saver.save(session, "qlearning.ckpt", global_step=t)
  
              # Print end of episode stats
              if terminal:
                  stats = [
                      ep_reward,
                      episode_ave_max_q / float(ep_t),
                      epsilon,
                  ]
                  for i in range(len(stats)):
                      session.run(
                          assign_ops[i],
                          {summary_placeholders[i]: float(stats[i])},
                      )
                  print(
                      "| Thread %.2i" % int(thread_id),
                      "| Step",
                      t,
                      "| Reward: %.2i" % int(ep_reward),
                      " Qmax: %.4f" % (episode_ave_max_q / float(ep_t)),
                      " Epsilon: %.5f" % epsilon,
                      " Epsilon progress: %.6f"
                      % (t / float(anneal_epsilon_timesteps)),
                  )
                  break
  
imports: from __future__ import division, print_function, absolute_import
  import threading
  import random
  import numpy
  import time
  from skimage.transform import resize
  from skimage.color import rgb2gray
  from collections import deque
  import gym
  import tensorflow.compat.v1
  import tflearn
label_desc: Use image node_id 3 for calling a global function with example usage: actor_learner_thread(thread_id, env, session, graph_ops, num_actions, summary_ops, saver) without return types