def experiment(test_game, num_experiments):
"""
Main experiment method that runs the Value Iteration experiments and prints results
works by learning a model x number of times.
the average number of moves per policy is then created and averaged per experiment
prints and returns the average number of episodes to reach the goal along with the learned policy.
"""
average_number_of_moves_with_policy = []
for x in range(num_experiments):
# Learn Policy
vi = ValueIteration(test_game)
policy_and_num_iterations = vi.value_iteration()
policy = policy_and_num_iterations[0]
print(policy)
avg_num_steps = 0
for itter in range(100):
num_steps = vi.execute_policy(policy)
avg_num_steps += num_steps
avg_num_steps /= 100.0
average_number_of_moves_with_policy.append(avg_num_steps)
total_average_num_steps = sum(average_number_of_moves_with_policy) / num_experiments
print("Total Average Number of Steps: {}".format(total_average_num_steps))
return total_average_num_steps
