decided to fuck everything up

.gitignore

@@ -6,3 +6,6 @@
 
 # Inputs
 *.txt
+
+# Latex
+build/

main.py

@@ -13,10 +13,9 @@ mpl.use('TkAgg')  # fixes my macOS bug
 import matplotlib.pyplot as plt
 
 
-P = 0.1
-ALPHA = 0.90
-EPSILON = 1e-12
-# EPSILON = 1e-12  # Convergence criterium
+P = 0.1  # Slip probability
+ALPHA = 0.90  # Discount factor
+
 A2 = np.array([  # Action index to action mapping
     [-1,  0],  # Up
     [ 1,  0],  # Down
@@ -31,7 +30,7 @@ S_TO_IJ = None  # Mapping of state vector to coordinates
 SN = None  # Number of states
 U_OF_X = None  # The allowed action space matrix representation
 PW_OF_X_U = None  # The probability distribution of disturbance
-G1_X = None  # The cost function vector representation (depends only on state)
+G1_X = None  # The cost function vector representation
 G2_X = None  # The second cost function vector representation
 F_X_U_W = None  # The System Equation
 
@@ -63,6 +62,7 @@ def init_global(maze_filename):
 
     S_TO_IJ = np.indices(MAZE.shape).transpose(1, 2, 0)[state_mask]
     SN = len(S_TO_IJ)
+
     ij_to_s = np.zeros(MAZE.shape, dtype=np.int32)
     ij_to_s[state_mask] = np.arange(SN)
 
@@ -99,6 +99,12 @@ def init_global(maze_filename):
 
 
 def plot_j_policy_on_maze(j, policy):
+    j_norm = (j - j.min()) / (j.max() - j.min()) + 1e-50
+    j_log = np.log10(j_norm)
+    print(j)
+    print(j_norm)
+    print(j_log)
+    print('-' * 50)
     heatmap = np.full(MAZE.shape, np.nan)
     heatmap[S_TO_IJ[:, 0], S_TO_IJ[:, 1]] = j
     cmap = mpl.cm.get_cmap('coolwarm')
@@ -147,13 +153,16 @@ def policy_iteration(j, g):
     return policy, j
 
 
-def _terminate(j, j_old, policy, policy_old):
-    # eps = EPSILON
-    # return np.abs(j - j_old).max() < eps
+def _terminate_pi(j, j_old, policy, policy_old):
     return np.all(policy == policy_old)
 
 
-def dynamic_programming(optimizer_step, g, return_history=False):
+def _terminate_vi(j, j_old, policy, policy_old):
+    eps = ALPHA**SN
+    return np.abs(j - j_old).max() < eps
+
+
+def dynamic_programming(optimizer_step, g, terminator, return_history=False):
     j = np.zeros(SN, dtype=np.float64)
     policy = None
     history = []
@@ -163,7 +172,7 @@ def dynamic_programming(optimizer_step, g, return_history=False):
         policy, j = optimizer_step(j, g)
         if return_history:
             history.append(j)
-        if _terminate(j, j_old, policy, policy_old):
+        if terminator(j, j_old, policy, policy_old):
             break
     if not return_history:
         return j, policy
@@ -185,19 +194,20 @@ if __name__ == '__main__':
     costs = {'g1': G1_X, 'g2': G2_X}
     optimizers = {'Value Iteration': value_iteration,
                   'Policy Iteration': policy_iteration}
+    terminators = {'Value Iteration': _terminate_vi,
+                   'Policy Iteration': _terminate_pi}
 
     for a in [0.9, 0.5, 0.01]:
-        plt.figure()
+        plt.figure(figsize=(9, 6))
+        plt.subplots_adjust(top=0.9, bottom=0.05, left=0.05, right=0.95)
         plt.suptitle('DISCOUNT = ' + str(a))
         i = 1
         for opt in ['Value Iteration', 'Policy Iteration']:
             for cost in ['g1', 'g2']:
-                name = ' / '.join([opt, cost])
+                name = '{} / {}'.format(opt, cost)
                 ALPHA = a
-                j, policy = dynamic_programming(optimizers[opt], costs[cost])
-                print(name)
-                print(j)
-                # print(name, j)
+                j, policy = dynamic_programming(optimizers[opt], costs[cost],
+                                                terminators[opt])
                 plt.subplot(2, 2, i)
                 plt.gca().set_title(name)
                 plot_j_policy_on_maze(j, policy)
@@ -205,15 +215,16 @@ if __name__ == '__main__':
 
     # Error graphs
     for opt in ['Value Iteration', 'Policy Iteration']:
-        plt.figure()
-        plt.subplots_adjust(wspace=0.45, hspace=0.45)
+        plt.figure(figsize=(9, 6))
+        plt.subplots_adjust(wspace=0.4, hspace=0.4)
         plt.suptitle(opt)
         i = 1
         for cost in ['g1', 'g2']:
-            for a in [0.9, 0.8, 0.7]:
+            for a in [0.99, 0.7, 0.5]:
                 name = 'Cost: {}, discount: {}'.format(cost, a)
                 ALPHA = a
                 history = dynamic_programming(optimizers[opt], costs[cost],
+                                              terminators[opt],
                                               return_history=True)
                 plt.subplot(2, 3, i)
                 plt.gca().set_title(name)

report.latex

@@ -0,0 +1,20 @@
+\documentclass{article}
+\usepackage[a4paper, margin=1in]{geometry}
+\usepackage{amsmath}
+\usepackage{fancyhdr}
+\pagestyle{fancy}
+\usepackage{lastpage}
+\cfoot{Page \thepage\ of \pageref{LastPage}}
+\rhead{Pavel Lutskov, 03654990}
+\lhead{Programming Assignment}
+\title{\huge Approximate Dynamic Programming and Reinforcement Learning \\
+  \Large Programming Assignment}
+% \subtitle{Assignment 1}
+\author{Pavel Lutskov, 03654990}
+\begin{document}
+\maketitle
+
+\section{Environment modeling}
+
+Blya ya zamodeliroval environment.
+\end{document}