def main():
throw.init_board()
num_games = 1000
#************************************************#
# Uncomment the lines below to run the mdp code, #
# using the simple dart thrower that matches #
# the thrower specified in question 2. #
#*************************************************
# Default is to solve MDP and play 1 game
throw.use_simple_thrower()
test(100, "mdp")
#*************************************************#
# Uncomment the lines below to run the modelbased #
# code using the complex dart thrower. #
#*************************************************#
# Seed the random number generator -- the default is
# the current system time. Enter a specific number
# into seed() to keep the dart thrower constant across
# multiple calls to main().
# Then, initialize the throwing model and run
# the modelbased algorithm.
random.seed(181)
throw.init_thrower()
f = open("q4a_data_strat1.csv", "w")
f.write("EPOCH_SIZE, AVG_TURNS\n")
avg_turns = modelbased.modelbased(GAMMA, 5, 100)
f.write("{0}, {1}\n".format(1, avg_turns))
def main():
throw.init_board()
num_games = 1000
#************************************************#
# Uncomment the lines below to run the mdp code, #
# using the simple dart thrower that matches #
# the thrower specified in question 2. #
#*************************************************
# Default is to solve MDP and play 1 game
throw.use_simple_thrower()
test(100, "mdp")
#*************************************************#
# Uncomment the lines below to run the modelbased #
# code using the complex dart thrower. #
#*************************************************#
# Seed the random number generator -- the default is
# the current system time. Enter a specific number
# into seed() to keep the dart thrower constant across
# multiple calls to main().
# Then, initialize the throwing model and run
# the modelbased algorithm.
random.seed(181)
throw.init_thrower()
f = open("q4a_data_strat1.csv", "w")
f.write("EPOCH_SIZE, AVG_TURNS\n")
avg_turns = modelbased.modelbased(GAMMA, 5, 100)
f.write("{0}, {1}\n".format(1, avg_turns))
def main():
throw.init_board()
num_games = 1000
#************************************************#
# Uncomment the lines below to run the mdp code, #
# using the simple dart thrower that matches #
# the thrower specified in question 2. #
#*************************************************
# Default is to solve MDP and play 1 game
throw.use_simple_thrower()
test(1, "mdp")
def main():
throw.init_board()
num_games = 1000
#************************************************#
# Uncomment the lines below to run the mdp code, #
# using the simple dart thrower that matches #
# the thrower specified in question 2. #
#*************************************************
# Default is to solve MDP and play 1 game
throw.use_simple_thrower()
test(1, "mdp")
def main():
scores = []
throw.init_board()
num_games = 100
#************************************************#
# Uncomment the lines below to run the mdp code, #
# using the simple dart thrower that matches #
# the thrower specified in question 2. #
#*************************************************
#Default is to solve MDP and play 1 game
throw.use_simple_thrower()
test(1, "mdp")
#*************************************************#
# Uncomment the lines below to run the modelbased #
# code using the complex dart thrower. #
#*************************************************#
# Seed the random number generator -- the default is
# the current system time. Enter a specific number
# into seed() to keep the dart thrower constant across
# multiple calls to main().
# Then, initialize the throwing model and run
# the modelbased algorithm.
# sizes = [1,5,10,15]
# for i in sizes:
# print "EPOCH SIZE: ", i
# random.seed()
# throw.init_thrower()
# modelbased.modelbased(GAMMA, i, num_games)
#*************************************************#
# Uncomment the lines below to run the modelfree #
# code using the complex dart thrower. #
#*************************************************#
# Plays 1 game using a default player. No modelfree
# code is provided.
learning = [0.8, 0.85, 0.9, 0.95, 1]
for l in learning:
print "LEARNING RATE: ", l
random.seed()
throw.init_thrower()
test(1000, "modelfree", l)
def task(self):
throw.init_board()
throw.use_simple_thrower()
y1= darts.test(1, "mdp")
throw.init_board()
throw.use_simple_thrower()
y2=darts.test(5, "mdp")
throw.init_board()
throw.use_simple_thrower()
y3=darts.test(10, "mdp")
listNames = ["1 game", "5 games", "10 games"]
listData = [y1, y2, y3]
chart = {"chart": {"defaultSeriesType": "column"},
"xAxis": {"categories": listNames},
"yAxis": {"title": {"text": "#Throws"}},
"title": {"text": "Average #throws to finish vs. #games"},
"series": [ {"name": "Average policy performance",
"data": listData} ] }
return chart
def task(self):
throw.init_board()
throw.use_simple_thrower()
y1 = darts.test(1, "mdp")
throw.init_board()
throw.use_simple_thrower()
y2 = darts.test(5, "mdp")
throw.init_board()
throw.use_simple_thrower()
y3 = darts.test(10, "mdp")
listNames = ["1 game", "5 games", "10 games"]
listData = [y1, y2, y3]
chart = {
"chart": {
"defaultSeriesType": "column"
},
"xAxis": {
"categories": listNames
},
"yAxis": {
"title": {
"text": "#Throws"
}
},
"title": {
"text": "Average #throws to finish vs. #games"
},
"series": [{
"name": "Average policy performance",
"data": listData
}]
}
print darts.GAMMA
return chart