Author | zdg |

Submission date | 2012-05-06 00:00:43.369429 |

Rating | 7624 |

Matches played | 632 |

Win rate | 78.8 |

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

```
# greedy history pattern match
# greedy - use highest available order and most recent
# order 6 - should be slightly faster than order 7 but just as good
# use my hands, op hands, as well as both to predict next hands
# update only the orders that are >= the order last used to make the prediction
# - something from PPM compression
# choose the strategy with the best score
# choose the meta strategy using an additional history matching corrector
# which matches using past meta choices and the corresponding payoffs
# even though I use a history matcher, it seems order 1 is the best
# a little bit of decay goes a long way
# this bot is mostly deterministic
# this beats bayes14 and bayes15 approximately 70% - 80% of the time, but it's
# still not as effective and ties a lot more with other bots
# --------------------- initialization -----------------------------
if not input:
import random, collections
# micro-optimizations
rchoice = random.choice
randint = random.randint
uniform = random.uniform
# global constants and maps
R, P, S = 0, 1, 2
RPS = [R, P, S]
T, W, L = 0, 1, 2
PAYOFFS = [T, W, L]
tonum = {'R':R, 'P':P, 'S':S}
tostr = {R:'R', P:'P', S:'S'}
scoreround = [[T, L, W], [W, T, L], [L, W, T]]
frompayoff = [[R, P, S], [P, S, R], [S, R, P]]
ties, beats, loses = frompayoff[T], frompayoff[W], frompayoff[L]
# more specific variables
topoints = [0, 1, -1]
encode1hand = [1,2,3]
decode1hand = [None, R, P, S]
encode2hands = [[1,2,3], [4,5,6], [7,8,9]]
decode2hands = [None,(R,R),(R,P),(R,S),(P,R),(P,P),(P,S),(S,R),(S,P),(S,S)]
encodemeta = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12], [13, 14, 15, 16, 17, 18]]
ORDER = 6
CORRECTOR_ORDER = 1
power3 = [0] + [3 ** i for i in xrange(ORDER)]
power9 = [0] + [9 ** i for i in xrange(ORDER)]
power18 = [0] + [18 ** i for i in xrange(CORRECTOR_ORDER)]
my_history = collections.defaultdict(lambda: None)
op_history = collections.defaultdict(lambda: None)
both_history = collections.defaultdict(lambda: None)
corrector_history = collections.defaultdict(lambda: None)
corrector_history[0] = [0] * 6
corrector_predict = corrector_history[0]
my_last_used_order = 0
op_last_used_order = 0
both_last_used_order = 0
corrector_last_used_order = 0
DECAY = 0.94
STRATEGIES = 18
META_DECAY = 0.94
META_STRATEGIES = 6
# 0-2 is my my, 3-5 is my op, 6-8 is both op my, 9-11 is op op, 12-14 is both my, 15-17 is both op
strategy_scores = [0.0] * STRATEGIES
reverse_strategy_scores = [0.0] * STRATEGIES
next_hands = [None] * STRATEGIES
next_hands_meta = [None] * META_STRATEGIES
meta_picks = []
# bookkeeping
my_hands_encoded = []
op_hands_encoded = []
both_hands_encoded = []
hands_played = 0
# first hand
output = tostr[rchoice(RPS)]
# --------------------- turn -----------------------------
else:
# bookkeeping
last_hand_index = hands_played
hands_played += 1
my_last_hand = tonum[output]
my_last_hand_encoded = encode1hand[my_last_hand]
op_last_hand = tonum[input]
op_last_hand_encoded = encode1hand[op_last_hand]
both_last_hand_encoded = encode2hands[my_last_hand][op_last_hand]
last_payoff = scoreround[my_last_hand][op_last_hand]
my_hands_encoded.append(my_last_hand_encoded)
op_hands_encoded.append(op_last_hand_encoded)
both_hands_encoded.append(both_last_hand_encoded)
# decay the scores
for i in xrange(STRATEGIES):
strategy_scores[i] *= DECAY
reverse_strategy_scores[i] *= DECAY
# update the scores of the strategies if played at least 2 hands
if hands_played > 1:
for i in xrange(STRATEGIES):
strategy_scores[i] += topoints[scoreround[next_hands[i]][op_last_hand]]
reverse_strategy_scores[i] += topoints[scoreround[next_hands[i]][my_last_hand]]
# update the corrector history
last_meta_pick = encodemeta[last_payoff][pick_meta_strategy]
meta_picks.append(last_meta_pick)
corrector_update_index = 0
if corrector_last_used_order == 0:
for i in xrange(META_STRATEGIES):
corrector_history[0][i] *= META_DECAY
corrector_history[0][i] += topoints[scoreround[next_hands_meta[i]][op_last_hand]]
corrector_predict = corrector_history[0]
for order in xrange(1, CORRECTOR_ORDER+1 if hands_played-1 > CORRECTOR_ORDER else hands_played-1):
corrector_predict_index = corrector_update_index * 18 + last_meta_pick
corrector_update_index += meta_picks[-order-1] * power18[order]
if order >= corrector_last_used_order:
if corrector_history[corrector_update_index] is None:
corrector_history[corrector_update_index] = [0] * 6
for i in xrange(META_STRATEGIES):
corrector_history[corrector_update_index][i] *= META_DECAY
corrector_history[corrector_update_index][i] += topoints[scoreround[next_hands_meta[i]][op_last_hand]]
corrector_try_get = corrector_history[corrector_predict_index]
if corrector_try_get is not None:
corrector_predict = corrector_try_get
corrector_last_used_order = order
# update the history, order 0 as a special case
my_update_index = 0
op_update_index = 0
both_update_index = 0
if my_last_used_order == 0:
my_history[0] = last_hand_index
if op_last_used_order == 0:
op_history[0] = last_hand_index
if both_last_used_order == 0:
both_history[0] = last_hand_index
# start the prediction with the zeroth order
my_predict = my_history[0]
op_predict = op_history[0]
both_predict = both_history[0]
# update the higher orders and predict the next hand
for order in xrange(1, ORDER+1 if hands_played > ORDER else hands_played):
my_predict_index = my_update_index * 3 + my_last_hand_encoded
op_predict_index = op_update_index * 3 + op_last_hand_encoded
both_predict_index = both_update_index * 9 + both_last_hand_encoded
my_update_index += my_hands_encoded[-order-1] * power3[order]
op_update_index += op_hands_encoded[-order-1] * power3[order]
both_update_index += both_hands_encoded[-order-1] * power9[order]
if order >= my_last_used_order:
my_history[my_update_index] = last_hand_index
if order >= op_last_used_order:
op_history[op_update_index] = last_hand_index
if order >= both_last_used_order:
both_history[both_update_index] = last_hand_index
my_try_get = my_history[my_predict_index]
op_try_get = op_history[op_predict_index]
both_try_get = both_history[both_predict_index]
if my_try_get is not None:
my_predict = my_try_get
my_last_used_order = order
if op_try_get is not None:
op_predict = op_try_get
op_last_used_order = order
if both_try_get is not None:
both_predict = both_try_get
both_last_used_order = order
my_base_prediction = decode2hands[both_hands_encoded[my_predict]]
op_base_prediction = decode2hands[both_hands_encoded[op_predict]]
both_base_prediction = decode2hands[both_hands_encoded[both_predict]]
# update each strategy's prediction
for f in PAYOFFS:
next_hands[f] = frompayoff[f][my_base_prediction[0]]
next_hands[f+3] = frompayoff[f][my_base_prediction[1]]
next_hands[f+6] = frompayoff[f][op_base_prediction[0]]
next_hands[f+9] = frompayoff[f][op_base_prediction[1]]
next_hands[f+12] = frompayoff[f][both_base_prediction[0]]
next_hands[f+15] = frompayoff[f][both_base_prediction[1]]
# use the strategy with the best score
max_score = max(strategy_scores)
max_list = [i for i in xrange(STRATEGIES) if strategy_scores[i] == max_score]
pick_strategy = rchoice(max_list)
# use the reverse strategy with the best score
max_reverse_score = max(reverse_strategy_scores)
max_reverse_list = [i for i in xrange(STRATEGIES) if reverse_strategy_scores[i] == max_reverse_score]
pick_reverse_strategy = rchoice(max_reverse_list)
# update the meta strategy
for f in PAYOFFS:
next_hands_meta[f] = frompayoff[f][next_hands[pick_strategy]]
next_hands_meta[f+3] = frompayoff[f][next_hands[pick_reverse_strategy]]
max_meta_score = max(corrector_predict)
max_meta_list = [i for i in xrange(META_STRATEGIES) if corrector_predict[i] == max_meta_score]
pick_meta_strategy = rchoice(max_meta_list)
# play the next hand
output = tostr[next_hands_meta[pick_meta_strategy]]
```