Author | zdg |

Submission date | 2012-05-17 19:09:41.133806 |

Rating | 8109 |

Matches played | 522 |

Win rate | 79.12 |

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

```
# testing out new strategies
# Name: zai_all_mix_meta
# AUthor: zdg
# Email: rpscontest.b73@gishpuppy.com
# the email is disposable in case it gets spammed
#
# the base strategy is to mix multiple strategies together instead of switching
# then on top of that, switches on the 6 meta strategies
# gets beaten by more adaptive bots like zai_ghpm7all_switch_meta
# but hopefully is more effective against less complex bots
# --------------------- initialization -----------------------------
if not input:
import random, collections, math
# micro-optimizations
rchoice = random.choice
randint = random.randint
runit = random.random
log = math.log
sqrt = math.sqrt
# global constants and maps
# using lists and dictionaries because function call and arithmetic is slow
R, P, S = 0, 1, 2
RPS = [R, P, S]
T, W, L = R, P, S
PAYOFFS = RPS
tr = {'R':R, 'P':P, 'S':S, R:'R', P:'P', S:'S'}
sub = [[T, L, W], [W, T, L], [L, W, T]]
add = [[R, P, S], [P, S, R], [S, R, P]]
ties, beats, loses = add[T], add[W], add[L]
pts = [0, 1, -1]
near = [1, 0, 0]
enc1 = [1,2,3]
dec1 = [None, R, P, S]
enc2 = [[1,2,3], [4,5,6], [7,8,9]]
dec2 = [None,(R,R),(R,P),(R,S),(P,R),(P,P),(P,S),(S,R),(S,P),(S,S)]
def pick_max(vec):
max_val = max(vec)
max_list = [i for i in xrange(len(vec)) if vec[i] == max_val]
return rchoice(max_list)
def pick_weighted(vec):
total = sum(vec) + 0.0
u = runit() * total
acc = 0.0
for i in xrange(len(vec)):
acc += vec[i]
if u < acc:
return i
else:
return randint(0, len(vec)-1)
# calculate the hand with the best expected value against the given op hand
# random only in case of ties
def expected(vec):
expected_payoffs = [vec[S] - vec[P], vec[R] - vec[S], vec[P] - vec[R]]
max_expected = max(expected_payoffs)
max_list = [i for i in xrange(3) if expected_payoffs[i] == max_expected]
return rchoice(max_list)
# greedy history pattern matcher
# ORDER is the largest context size
# BASE is the base of the numerical encoding
# encodes sequences of numbers from 1...BASE as a BASE-adic number
# encodes the empty sequence as 0
# apparently this encoding is called a bijective base-BASE system on wikipedia
class GHPM:
def __init__(self, ORDER, BASE):
self.ORDER = ORDER
self.BASE = BASE
self.powers = [0] + [BASE ** i for i in xrange(ORDER)]
self.hist = []
self.contexts = collections.defaultdict(lambda: None)
self.pred = None
def update(self, next_val, up_val):
self.hist.append(next_val)
# update the history, order 0 as a special case
up_ix = 0
self.contexts[0] = up_val
# start the prediction with the zeroth order
self.pred = self.contexts[0]
# update the higher orders and prediction
elems = len(self.hist)
for order in xrange(1, self.ORDER+1 if elems > self.ORDER else elems):
pred_ix = up_ix * self.BASE + next_val
up_ix += self.hist[-order-1] * self.powers[order]
self.contexts[up_ix] = up_val
try_get = self.contexts[pred_ix]
if try_get is not None:
self.pred = try_get
# more specific globals
STRATEGIES = range(27)
my_ghpm = GHPM(6, 3)
op_ghpm = GHPM(6, 3)
both_ghpm = GHPM(6, 9)
DECAY = 0.98
sscores = [0 for _ in STRATEGIES]
rev_sscores = [0 for _ in STRATEGIES]
next_hands = [None for _ in STRATEGIES]
PERIOD = 1
META_DECAY = 0.98
META_STRATEGIES = range(6)
meta_sscores = [0 for _ in META_STRATEGIES]
meta_next = [None for _ in META_STRATEGIES]
meta_pick = 0
# SWITCH_DECAY = 0.98
# switch_scores = [0,0]
# constant bot
next_hands[0] = R
# first hand is completely random - no reason to do otherwise
output = tr[rchoice(RPS)]
# bookkeeping
hands = 1
last_ix = 0
score = 0
# --------------------- turn -----------------------------
else:
last_my = tr[output]
last_op = tr[input]
last_payoff = sub[last_my][last_op]
# update the history matchers
my_ghpm.update(enc1[last_my], last_ix)
op_ghpm.update(enc1[last_op], last_ix)
both_ghpm.update(enc2[last_my][last_op], last_ix)
if hands > 1:
# update the scores
for i in STRATEGIES:
sscores[i] *= DECAY
# sscores[i] += pts[sub[next_hands[i]][last_op]]
sscores[i] += near[sub[next_hands[i]][last_op]]
rev_sscores[i] *= DECAY
# rev_sscores[i] += pts[sub[next_hands[i]][last_my]]
rev_sscores[i] += near[sub[next_hands[i]][last_my]]
for i in META_STRATEGIES:
meta_sscores[i] *= META_DECAY
meta_sscores[i] += pts[sub[meta_next[i]][last_op]]
# update next_hands
# constant needs no update
# use only last
next_hands[3] = last_my
next_hands[6] = last_op
# the bigger strategies
both_hist = both_ghpm.hist
pred_op, pred_my = dec2[both_hist[my_ghpm.pred]]
next_hands[9] = pred_op
next_hands[12] = pred_my
pred_op, pred_my = dec2[both_hist[op_ghpm.pred]]
next_hands[15] = pred_op
next_hands[18] = pred_my
pred_op, pred_my = dec2[both_hist[both_ghpm.pred]]
next_hands[21] = pred_op
next_hands[24] = pred_my
# the other indices are just based on the hands at the multiples of 3
for i in STRATEGIES:
if i % 3 != 0:
next_hands[i] = beats[next_hands[i-1]]
# predict next op
next_prob = [0, 0, 0]
for i in STRATEGIES:
next_prob[next_hands[i]] += sscores[i]
# next_my = pick_max(next_prob)
# next_my_weighted = beats[pick_weighted(next_prob)]
next_my = expected(next_prob)
# predict next my
rev_next_prob = [0, 0, 0]
for i in STRATEGIES:
rev_next_prob[next_hands[i]] += rev_sscores[i]
rev_next_my = expected(rev_next_prob)
# update meta
meta_next[0] = next_my
meta_next[3] = rev_next_my
for i in META_STRATEGIES:
if i % 3 != 0:
meta_next[i] = beats[meta_next[i-1]]
meta_pick = pick_max(meta_sscores)
meta_next_my = meta_next[meta_pick]
output = tr[meta_next_my]
# output = tr[next_my]
# bookkeeping
hands += 1
last_ix += 1
score += pts[last_payoff]
# if hands % 100 == 0:
# print PERIOD
# print '-' * 80
# print META_DECAY
```