def main(algorithm, track, x_start, y_start, discount, learning_rate, threshold, max_iterations, epsilon=None, reset_on_crash=False):
"""
Program entry. Runs selected algorithm on selected track, at given coordinates, with given parameters
:param algorithm: String
:param track: List
:param x_start: Int
:param y_start: Int
:param discount: Float
:param learning_rate: Float
:param threshold: Float
:param max_iterations: Int
:param epsilon: Float
:param reset_on_crash: Boolean
:return: None
"""
with open(track) as f:
specs = f.readline().strip().split(',')
rows = int(specs[0])
cols = int(specs[1])
layout = f.read().splitlines()
initial_state = (x_start, y_start, 0, 0)
initial_action = (0, 0)
agent = Car(initial_action, epsilon)
environment = RaceTrack(rows, cols, layout, initial_state, reset_on_crash=reset_on_crash)
if algorithm == 'value_iteration':
value_iterator = ValueIteration(discount, threshold, max_iterations, environment, agent)
value_iterator.run()
path = value_iterator.extract_policy(initial_state)
value_iterator.plot_max_diffs()
elif algorithm == 'q_learning':
q_learner = QLearning(discount, learning_rate, threshold, max_iterations, environment, agent)
path = q_learner.run()
q_learner.plot_avg_cost()
elif algorithm == 'sarsa':
sarsa = Sarsa(discount, learning_rate, threshold, max_iterations, environment, agent)
path = sarsa.run()
sarsa.plot_avg_cost()
else:
print("No algorithm selected")
return None
draw_track(path, layout)
def main(args):
# resolve path to world map definition
if not args.world:
world_map_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)), 'world_map.txt')
else:
world_map_path = args.world
print("Reading world from %s" % world_map_path)
if not os.path.exists(world_map_path):
raise IOError(
"World map definition not found at its expected path: %s" %
world_map_path)
world = World(world_map_path)
visualizer = Visualizer(world)
# Value Iteration
value_iteration = ValueIteration(world,
one_step_cost_v1,
discount_factor=args.gamma,
eps=10e-10)
value_iteration.execute()
optimal_policy = value_iteration.extract_policy()
fig_vi = plt.figure()
visualizer.draw(fig_vi, optimal_policy, value_iteration.value_fn,
"Value Iteration (gamma = %.2f)" % args.gamma)
# Policy Iteration
policy_iteration = PolicyIteration(world,
one_step_cost_v1,
discount_factor=args.gamma)
value_fn = policy_iteration.execute()
fig_pi = plt.figure()
visualizer.draw(fig_pi, policy_iteration.policy, value_fn,
"Policy Iteration (gamma = %.2f)" % args.gamma)
plt.show()