# RL_features

 Author Yang-12 Submission date 2016-11-13 04:47:07.468766 Rating 7394 Matches played 401 Win rate 74.56

Use rpsrunner.py to play unranked matches on your computer.

## Source code:

``````import random

nwin = 0
ntie = 0
nloss = 0
iter = 0
epsilon = 0.2
#input = ""
#while True:
if not input:
score = {
('R', 'R'): 0, ('R', 'P'): -1, ('R', 'S'): 1,
('P', 'R'): 1, ('P', 'P'): 0, ('P', 'S'): -1,
('S', 'R'): -1, ('S', 'P'): 1, ('S', 'S'): 0
}
Q = dict()
lr = 0.9
limits = [5, 15, 30]
beat = {'R': 'P', 'P': 'S', 'S': 'R'}
urmoves = ""
mymoves = ""
DNAmoves = ""
output = random.choice(['R', 'P', 'S'])
nuclease = {'RP': 'a', 'PS': 'b', 'SR': 'c', 'PR': 'd', 'SP': 'e', 'RS': 'f', 'RR': 'g', 'PP': 'h', 'SS': 'i'}
length = 0
output = random.choice('RPS')
newstate = tuple()
else:
# History matching
urmoves += input
mymoves += output
DNAmoves += nuclease[input + output]
length += 1

state = newstate
newstate = []
for z in range(1):
limit = min([length, limits[z]])
j = limit
while j >= 1 and not DNAmoves[length - j:length] in DNAmoves[0:length - 1]:
j -= 1
if j >= 1:
i = DNAmoves.rfind(DNAmoves[length - j:length], 0, length - 1)  # You seem to be playing based on our moves
newstate.append(urmoves[j + i])
newstate.append(mymoves[j + i])
j = limit
while j >= 1 and not urmoves[length - j:length] in urmoves[0:length - 1]:
j -= 1
if j >= 1:
i = urmoves.rfind(urmoves[length - j:length], 0, length - 1)  # You seem to be playing based on your moves
newstate.append(urmoves[j+i])
newstate.append(mymoves[j+i])
j = limit
while j >= 1 and not mymoves[length - j:length] in mymoves[0:length - 1]:
j -= 1
if j >= 1:
i = mymoves.rfind(mymoves[length - j:length], 0, length - 1)  # You seem to be playing based on my moves
newstate.append(urmoves[j+i])
newstate.append(mymoves[j+i])

newstate = tuple(newstate)
action = output
#        print "program gives: %s" % output
if score[(output, input)] == 1: nloss += 1
elif score[(output, input)] == 0: ntie += 1
elif score[(output, input)] == -1: nwin += 1

reward = score[(action, input)]
maxvalue = max(Q.get((newstate, a), 0) for a in 'RPS')
Q[(state, action)] = Q.get((state, action), 0) + lr * (reward + 0.5 * maxvalue - Q.get((state, action), 0))
succ = [Q.get((newstate, a), 0) for a in 'RPS']
optimal_actions = ['RPS'[x] for x in range(len(succ)) if succ[x] == max(succ)]
output = random.choice(optimal_actions) if random.random() > epsilon else random.choice('RPS')``````