print(policy_grids[:, :, -1])
print(utility_grids[:, :, -1])
plt.figure()
gw.plot_policy(utility_grids[:, :, -1])
plot_convergence(utility_grids, policy_grids)
plt.show()
ql = QLearner(num_states=(shape[0] * shape[1]),
num_actions=4,
learning_rate=0.8,
discount_rate=0.9,
random_action_prob=0.5,
random_action_decay_rate=0.99,
dyna_iterations=0)
start_state = gw.grid_coordinates_to_indices(start)
iterations = 1000
flat_policies, flat_utilities = ql.learn(start_state,
gw.generate_experience,
iterations=iterations)
new_shape = (gw.shape[0], gw.shape[1], iterations)
ql_utility_grids = flat_utilities.reshape(new_shape)
ql_policy_grids = flat_policies.reshape(new_shape)
print('Final result of QLearning:')
print(ql_policy_grids[:, :, -1])
print(ql_utility_grids[:, :, -1])
plt.figure()
gw.plot_policy(ql_utility_grids[:, :, -1], ql_policy_grids[:, :, -1])
