Relocated the file and fixed issue in ending the training phase

discrete_dppo.py → contents/12_Proximal_Policy_Optimization/discrete_DPP0.py

@@ -18,8 +18,9 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import gym, threading, queue
+import time
 
-EP_MAX = 4000
+EP_MAX = 1000
 EP_LEN = 500
 N_WORKER = 4                # parallel workers
 GAMMA = 0.9                 # reward discount factor
@@ -171,14 +172,15 @@ def work(self):
 
                     QUEUE.put(q_in)
 
-                    if GLOBAL_UPDATE_COUNTER >= MIN_BATCH_SIZE or done:
+                    if GLOBAL_UPDATE_COUNTER >= MIN_BATCH_SIZE:
                         ROLLING_EVENT.clear()       # stop collecting data
                         UPDATE_EVENT.set()          # globalPPO update
-                        break
 
                     if GLOBAL_EP >= EP_MAX:         # stop training
                         COORD.request_stop()
                         break
+
+                    if done:break
 
             # record reward changes, plot later
             if len(GLOBAL_RUNNING_R) == 0: GLOBAL_RUNNING_R.append(ep_r)
@@ -187,12 +189,6 @@ def work(self):
             print("EP", GLOBAL_EP,'|W%i' % self.wid, '|step %i' %t, '|Ep_r: %.2f' % ep_r,)
             np.save("Global_return",GLOBAL_RUNNING_R)
             np.savez("PI_PARA",self.ppo.sess.run(GLOBAL_PPO.pi_params))
-#            np.savez("tfa",self.ppo.sess.run(GLOBAL_PPO.tfa))
-#            np.savez("tfadv",self.ppo.sess.run(GLOBAL_PPO.tfadv))
-#            np.savez("val1",self.ppo.sess.run(GLOBAL_PPO.val1))
-#            np.savez("val2",self.ppo.sess.run(GLOBAL_PPO.val2))
-#            print self.ppo.sess.run(GLOBAL_PPO.val2)
-
 
 
 if __name__ == '__main__':
@@ -202,6 +198,8 @@ def work(self):
     ROLLING_EVENT.set()             # start to roll out
     workers = [Worker(wid=i) for i in range(N_WORKER)]
 
+    start = time.time()
+
     GLOBAL_UPDATE_COUNTER, GLOBAL_EP = 0, 0
     GLOBAL_RUNNING_R = []
     COORD = tf.train.Coordinator()
@@ -216,6 +214,9 @@ def work(self):
     threads[-1].start()
     COORD.join(threads)
 
+    end = time.time()
+    print "Total time ", (end - start)
+
     # plot reward change and test
     plt.plot(np.arange(len(GLOBAL_RUNNING_R)), GLOBAL_RUNNING_R)
     plt.xlabel('Episode'); plt.ylabel('Moving reward'); plt.ion(); plt.show()
@@ -224,6 +225,7 @@ def work(self):
         s = env.reset()
         for t in range(1000):
             env.render()
-            s = env.step(GLOBAL_PPO.choose_action(s))[0]
+            s, r, done, info = env.step(GLOBAL_PPO.choose_action(s))
             if done:
                 break
+