Delete BellmanDPBase.py

BellmanDPBase.py

-from MDP import MDP
-import numpy as np
-import copy
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-
-def plot_value_and_policy(values, policy):
-
-
-    data = np.zeros((5, 5))
-
-    plt.figure(figsize=(12, 4))
-    plt.subplot(1, 2, 1)
-    plt.title('Value')
-    for y in range(data.shape[0]):
-        for x in range(data.shape[1]):
-            data[y][x] = values[(x, y)]
-            plt.text(x + 0.5, y + 0.5, '%.4f' % data[y, x], horizontalalignment='center', verticalalignment='center', )
-
-    heatmap = plt.pcolor(data)
-    plt.gca().invert_yaxis()
-    plt.colorbar(heatmap)
-
-    plt.subplot(1, 2, 2)
-    plt.title('Policy')
-    for y in range(5):
-        for x in range(5):
-            for action in policy[(x, y)]:
-                if action == 'DRIBBLE_UP':
-                    plt.annotate('', (x + 0.5, y), (x + 0.5, y + 0.5), arrowprops={'width': 0.1})
-                if action == 'DRIBBLE_DOWN':
-                    plt.annotate('', (x + 0.5, y + 1), (x + 0.5, y + 0.5), arrowprops={'width': 0.1})
-                if action == 'DRIBBLE_RIGHT':
-                    plt.annotate('', (x + 1, y + 0.5), (x + 0.5, y + 0.5), arrowprops={'width': 0.1})
-                if action == 'DRIBBLE_LEFT':
-                    plt.annotate('', (x, y + 0.5), (x + 0.5, y + 0.5), arrowprops={'width': 0.1})
-                if action == 'SHOOT':
-                    plt.text(x + 0.5, y + 0.5, action, horizontalalignment='center', verticalalignment='center', )
-
-    heatmap = plt.pcolor(data)
-    plt.gca().invert_yaxis()
-    plt.colorbar(heatmap)
-    plt.show()
-
-
-class BellmanDPSolver(object):
-    def __init__(self, discountRate=0.9):
-        self.MDP = MDP()
-        self.discountRate = discountRate
-        self.initVs()
-
-    def initVs(self):
-        self.V = {}
-        self.policy = {}
-        for state in self.MDP.S:
-            self.V[state] = 0
-            self.policy[state] = np.array([0.5] * len(self.MDP.A))
-
-    def BellmanUpdate(self):
-        try:
-            copy_V = copy.deepcopy(self.V)
-            for state in self.MDP.S:
-                next_policy = []
-                nextValue = None
-                action_list = ["DRIBBLE_UP","DRIBBLE_DOWN","DRIBBLE_LEFT","DRIBBLE_RIGHT","SHOOT"];
-                for i in range(5):
-                    action = action_list[i];
-                    temp = 0
-                    for nextState, prob in self.MDP.probNextStates(state, action).items():
-                        temp += prob * (self.MDP.getRewards(state, action, nextState) + self.discountRate * copy_V[
-                            nextState])
-                    if not nextValue or nextValue == temp:
-                        next_policy.append(action)
-                        nextValue = temp
-                    elif nextValue > temp:
-                        continue
-                    else:
-                        next_policy = [action]
-                        nextValue = temp
-                self.V[state] = nextValue
-                self.policy[state] = next_policy
-
-            return self.V, self.policy
-        except:
-            raise NotImplementedError
-
-
-if __name__ == '__main__':
-    solution = BellmanDPSolver()
-    iter =1000
-    for i in range(iter):
-        values, policy = solution.BellmanUpdate()
-    print("Values : ", values)
-    print("Policy : ", policy)
-    print(iter)
-    plot_value_and_policy(values, policy)