def run_premade_map():
# Set up RL
data = Data(ROWS, COLS, load_data=False, epsilon=0.25)
gen = Generator(ROWS, COLS, resolution, size)
vis = Plotter(data, cell_edge, ROWS, COLS)
data.load_racetrack(directory='Sensitivity_Analysis')
data.get_start_line()
data.get_finish_line()
vis.visualize_racetrack()
# Finish setting up RL
env = Environment(data, gen, ROWS, COLS)
mcc = Monte_Carlo_Control(data, ROWS, COLS)
agent = Agent()
print("Saving data")
# Save arrays
data.save_racetrack(directory='Sensitivity_Analysis_2')
data.save_Q_vals(directory='Sensitivity_Analysis_2')
data.save_C_vals(directory='Sensitivity_Analysis_2')
data.save_rewards(directory='Sensitivity_Analysis_2')
data.save_pi(directory='Sensitivity_Analysis_2')
# Train agent
train_agent(mcc,
env,
agent,
data,
plot=True,
image_name='sensitivity_epsilon=0.25')
get_gif('epsilon=0.25', data, vis)
def test_map_making():
data = Data(ROWS, COLS, load_data=False)
gen = Generator(ROWS, COLS, resolution, size)
vis = Plotter(data, cell_edge, ROWS, COLS)
data.racetrack = gen.generate_map()
vis.visualize_racetrack()
def run_random_map(plot=False,
directory='Random_Map_Data',
image_name='random_reward_graph',
epsilon=0.1):
# Set up RL
data = Data(ROWS, COLS, load_data=False, epsilon=epsilon)
gen = Generator(ROWS, COLS, resolution, size)
vis = Plotter(data, cell_edge, ROWS, COLS)
# TODO:: Must be able to plot map without needing space bar
# Display maps until we get one we like
while True:
data.racetrack = gen.generate_map()
data.get_start_line()
data.get_finish_line()
vis.visualize_racetrack()
response = input("Is this map suitable for our test? (y/n): ")
if response == 'y':
break
# Finish setting up RL
env = Environment(data, gen, ROWS, COLS)
mcc = Monte_Carlo_Control(data, ROWS, COLS)
agent = Agent()
print("Saving data")
# Save arrays
data.save_racetrack(directory=directory)
data.save_Q_vals(directory=directory)
data.save_C_vals(directory=directory)
data.save_rewards(directory=directory)
data.save_pi(directory=directory)
# Train agent
train_agent(mcc, env, agent, data, plot=plot, image_name=image_name)
if plot:
get_gif(image_name, data, vis)
def sensitivity_analysis():
# Sensitivity analysis settings for epsilon=0.1
directory = 'Sensitivity_Analysis'
image_name = 'epsilon=0.1'
epsilon = 0.1
# Set up RL
data = Data(ROWS, COLS, load_data=False, epsilon=epsilon)
gen = Generator(ROWS, COLS, resolution, size)
vis = Plotter(data, cell_edge, ROWS, COLS)
# Display maps until we get one we like
while True:
data.racetrack = gen.generate_map()
data.get_start_line()
data.get_finish_line()
vis.visualize_racetrack()
response = input("Is this map suitable for our test? (y/n): ")
if response == 'y':
break
# Finish setting up RL
env = Environment(data, gen, ROWS, COLS)
mcc = Monte_Carlo_Control(data, ROWS, COLS)
agent = Agent()
print("Saving data")
# Save arrays
data.save_racetrack(directory=directory)
data.save_Q_vals(directory=directory)
data.save_C_vals(directory=directory)
data.save_rewards(directory=directory)
data.save_pi(directory=directory)
# Train agent
train_agent(mcc, env, agent, data, plot=True, image_name=image_name)
# Sensitivity analysis settings for epsilon=0.01
image_name = 'epsilon=0.01'
epsilon = 0.01
gif_name = 'epsilon=0.01'
# Set up RL
data2 = Data(ROWS, COLS, load_data=False, epsilon=epsilon)
vis = Plotter(data2, cell_edge, ROWS, COLS)
# Get old map
data2.racetrack = data.racetrack
data2.get_start_line()
data2.get_finish_line()
# Finish setting up RL
env = Environment(data2, gen, ROWS, COLS)
mcc = Monte_Carlo_Control(data2, ROWS, COLS)
print("Saving data")
# Save arrays
data.save_racetrack(directory=directory)
data.save_Q_vals(directory=directory)
data.save_C_vals(directory=directory)
data.save_rewards(directory=directory)
data.save_pi(directory=directory)
# Train agent
train_agent(mcc, env, agent, data2, plot=True, image_name=image_name)
get_gif(gif_name, data2, vis)
