8

Author: TarrySingh

deep-learning/Deep-Reinforcement-Learning-Complete-Collection/DeepRL-Code/chapter08/expectation_vs_sample.py

@@ -0,0 +1,54 @@
+#######################################################################
+# Copyright (C) #
+# 2018 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
+# Permission given to modify the code as long as you keep this #
+# declaration at the top #
+#######################################################################
+
+import numpy as np
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+
+# for figure 8.7, run a simulation of 2 * @b steps
+def b_steps(b):
+ # set the value of the next b states
+ # it is not clear how to set this
+ distribution = np.random.randn(b)
+
+ # true value of the current state
+ true_v = np.mean(distribution)
+
+ samples = []
+ errors = []
+
+ # sample 2b steps
+ for t in range(2 * b):
+ v = np.random.choice(distribution)
+ samples.append(v)
+ errors.append(np.abs(np.mean(samples) - true_v))
+
+ return errors
+
+def figure_8_7():
+ runs = 100
+ branch = [2, 10, 100, 1000]
+ for b in branch:
+ errors = np.zeros((runs, 2 * b))
+ for r in tqdm(np.arange(runs)):
+ errors[r] = b_steps(b)
+ errors = errors.mean(axis=0)
+ x_axis = (np.arange(len(errors)) + 1) / float(b)
+ plt.plot(x_axis, errors, label='b = %d' % (b))
+
+ plt.xlabel('number of computations')
+ plt.xticks([0, 1.0, 2.0], ['0', 'b', '2b'])
+ plt.ylabel('RMS error')
+ plt.legend()
+
+ plt.savefig('../images/figure_8_7.png')
+ plt.close()
+
+if __name__ == '__main__':
+ figure_8_7()