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)
import helper
from sarsa import Sarsa
from qlearning import QLearning
from sarsa_expected import SarsaExpected
# File to run in order to generate all the plots sequentially
if __name__ == '__main__':
data_X = np.arange(start=0, stop=10000, step=100)
# Agent 1: Sarsa(0)
data_Y1 = np.zeros((100, 1))
for seed in range(50):
print(f'Seed: {seed}')
sarsa = Sarsa(seed=seed, num_actions=4, alpha=0.1)
y = sarsa.run()
data_Y1 += y
data_Y1 /= 10
helper.plotSingle(data_X, data_Y1, "Sarsa(0)")
# Sarsa(0) with King's move
data_Y2 = np.zeros((100, 1))
for seed in range(50):
print(f'Seed: {seed}')
sarsa = Sarsa(seed=seed, num_actions=8, alpha=0.1)
y = sarsa.run()
data_Y2 += y
data_Y2 /= 10
helper.plotSingle(data_X, data_Y2, "Sarsa(0) with King's move")
# Sarsa(0) with stochastic wind
