Modified plot_evaluations.py with experimental labels and scaling. Still needs more tuning.

agents/plot_evaluations.py

@@ -2,7 +2,9 @@ from numpy import load
 import matplotlib.pyplot as plt 
 # import matplotlib.axes
 
-data = load("evaluations.npz")
+filepath = "dqn_v2-5/evaluations.npz"
+
+data = load(filepath)
 lst = data.files # data.files lists the keys that are available for data
 
 # print('ep_lengths: \n', data['ep_lengths'])
@@ -36,11 +38,15 @@ for eval in data['ep_lengths']:
         
     avg_ep_len = ep_len_sum / len(eval)
     avg_ep_len_arr.append(avg_ep_len)
-    y_limit = y_limit * 1.01
+    y_limit = y_limit * 1.9
     
     
-plt.plot(data['timesteps'], avg_ep_result_arr)
-plt.bar(data['timesteps'], avg_ep_len_arr, width=10000)
+x = plt.plot(data['timesteps'], avg_ep_result_arr)
+# plt.bar(data['timesteps'], avg_ep_len_arr, width=10000)
+y = plt.plot(data['timesteps'], avg_ep_len_arr)
 plt.ylim(top=y_limit)
-
+# plt.ylabel("Avg ep score")
+# lineObjects = plt.plot(x, y)
+plt.legend(["avg ep result", "avg ep length"])
+plt.title("result and length over steps\nfilepath: " + filepath)
 plt.show()