states_and_rewards = play_game(policy, grid)
for t in range(len(states_and_rewards) - 1):
s, _ = states_and_rewards[t]
s2, r = states_and_rewards[t + 1]
old_theta = model.theta.copy()
if grid.is_terminal(s2):
target = r
else:
target = r + GAMMA * model.predict(s2)
#x = model.s2x(s)
model.theta += alpha * (target - model.predict(s)) * model.grad(s)
biggest_change = max(biggest_change,
np.abs(old_theta - model.theta).sum())
deltas.append(biggest_change)
plt.plot(deltas)
plt.show()
#Predict V
V = {}
for s in states:
if s in grid.actions:
V[s] = model.predict(s)
else:
V[s] = 0
print("Values: ")
print_values(V, grid)
print("Policy: ")
print_policy(policy, grid)
for s in States:
if s == grid.actions:
V[s] = np.random.random()
else:
#Terminal States
V[s] = 0
#Initial policy
Policy = {}
for s in grid.actions.keys():
Policy[s] = np.random.choice(ALL_POSSIBLE_ACTIONS)
print('Print Policy: ')
print_policy(Policy, grid)
while True:
while True:# k : iteration
#Setting delta to say the new Value
#is the policy True Value or Not
delta = 0
for s in States:
old_v = V[s]
new_v = 0
if s in Policy:
#Check all the Posible actions
for a in ALL_POSSIBLE_ACTIONS:
#If the action is the action that the agent
#want to take then the probability is 0.5 otherwise
#the probability is 0.5/3 for each ofther actions