| Author | PotentProton |
| Submission date | 2017-02-27 01:34:04.807680 |
| Rating | 7089 |
| Matches played | 363 |
| Win rate | 69.42 |
Use rpsrunner.py to play unranked matches on your computer.
###############################################################################
# #
# This is Chainmail350, an RPS program written by PotentProton. #
# #
# This program is a modification on the program I have written named #
# AmateurSwordsman. It adds a randomized fall-back strategy designed to #
# guarantee a 35% win rate against even the toughest opponents. #
# #
# For more information on AmateurSwordsman, see #
# http://www.rpscontest.com/entry/5679790782676992 #
# #
# The random fall-back strategy uses thresholds which I have precomputed to #
# be the lowest possible score at any point in time in which a totally random #
# strategy has a 35% chance of a win. This type of strategy is superior to #
# other forms of defensive random fall-back strategies because of its ability #
# to completely guarantee a certain chance of success. Other strategies that #
# decide whether to do a random move based on recent performance of #
# predictors could experience very slow downwards trends without realizing #
# that in the long run they are losing. #
# #
# It is hard to know what is the best threshold to cut off at. Here I have #
# chosen to guarantee 35% success, but this comes at a cost. There is #
# inherently some randomness in Rock Paper Scissors, and this program runs a #
# risk of hitting thresholds at no fault of its own. It is also very hard to #
# optimize the threshold, as this would require running against the large #
# number of bad programs that we should be able to beat. I am therefore #
# submitting multiple versions of this Chainmail program. #
# #
# The program I used to pre-compute thresholds is included at the end of this #
# file, commented out. #
# #
###############################################################################
import random
if input == "":
##################################################
# Create read-only useful conversion structures. #
##################################################
# Convert input+output pair to shorthand:
iop2sh = {'RR':'0','RP':'1','RS':'2','PR':'3','PP':'4','PS':'5','SR':'6','SP':'7','SS':'8'}
# Get input from shorthand:
sh2i = { '0':'R', '1':'R', '2':'R', '3':'P', '4':'P', '5':'P', '6':'S', '7':'S', '8':'S'}
# Get output from shorthand:
sh2o = { '0':'R', '1':'P', '2':'S', '3':'R', '4':'P', '5':'S', '6':'R', '7':'P', '8':'S'}
# Get move that beats given move:
beat = {'R':'P','P':'S','S':'R'}
# Get move that loses to given move:
lose = {'R':'S','P':'R','S':'P'}
####################################
# Initialize more data structures. #
####################################
# Move history, for analysis:
hist = ''
# Current move number:
now = 0
# Current score:
score = 0
# Output selections and scores for meta strategies:
mo = ['X'] * 3
msc = [0.0] * 3
# Store whether we have switched to a 100% random strategy:
randstrat = 0
################
# Select move. #
################
# Since we have no data to go on, we use randomness.
output = random.choice(['R','P','S'])
else:
##################################
# Check threshold for randomness #
##################################
# Increment the move number:
now += 1
# Update the score:
if input == beat[output]:
score -= 1
elif output == beat[input]:
score += 1
# Switch to a random strategy if we risk not being able to guarantee a 35%
# chance of a win.
if score <= -9:
randstrat = 1
if now > 88 and score <= -8:
randstrat = 1
if now > 270 and score <= -7:
randstrat = 1
if now > 431 and score <= -6:
randstrat = 1
if now > 573 and score <= -5:
randstrat = 1
if now > 694 and score <= -4:
randstrat = 1
if now > 795 and score <= -3:
randstrat = 1
if now > 876 and score <= -2:
randstrat = 1
if now > 937 and score <= -1:
randstrat = 1
if now > 977 and score <= 0:
randstrat = 1
if now > 997 and score <= 1:
randstrat = 1
# Output random result if we have switched to randomness, otherwise
# continue the program as normal.
if randstrat:
output = random.choice(['R','P','S'])
else:
####################
# Update raw data. #
####################
# Record most recent input output pair:
hist += iop2sh[input+output]
# Update meta strategy scores. We add a decay multiplier of 0.9 to give
# more weight to more recent events.
for i in xrange(0,len(mo)):
# Check that there has actually been a prediction.
if mo[i] != 'X':
if mo[i] == beat[input]:
msc[i] += 1.0
elif mo[i] == lose[input]:
msc[i] -= 1.0
msc[i] *= 0.9
#######################
# Do pattern matching #
#######################
# We search for a sequence of events in the history which matches
# recent events. We try to match longer and longer strings until we
# fail. The Python rfind function starts at the end of the list and
# searches backwards, giving us the most recent occurrence of a
# sequence. Note that the for loop will not run if now < 2.
match = -1
for i in xrange(1,min(15,now-1)):
localmatch = hist[:(now-1)].rfind(hist[-i:])
if localmatch != -1:
# match was found, update with the new best match.
match = localmatch + i
else:
# no reason to continue, next iteration is strictly harder to
# match.
break
if match == -1:
# No match could be found, so we pick a random output.
output = random.choice(['R','P','S'])
# Also reset the meta moves so that we don't rescore moves twice.
mo = ['X'] * 3
else:
# Get shorthand of match from history.
shmatch = hist[match]
# Get input/output from shorthand of match.
imatch = sh2i[shmatch]
###########################################
# Select output using best meta strategy. #
###########################################
# Select outputs desired by different strategies.
mo[0] = imatch
mo[1] = beat[imatch]
mo[2] = lose[imatch]
# Find strategy with highest expected value.
maxmo = 'X' # Output of best meta-strategy.
maxmsc = -1001 # Score of best meta-strategy.
maxvalid = 0 # Maximum invalidated in case of conflicting tie.
for i in xrange(0,len(mo)):
if msc[i] > maxmsc:
maxmo = mo[i]
maxmsc = msc[i]
maxvalid = 1
elif (msc[i] == maxmsc) and (mo[i] != maxmo):
# In the case of a tie between meta strategies, and the
# meta strategies select different outputs, we should pick
# a random output instead.
maxvalid = 0
if maxvalid:
output = maxmo
else:
# Picking random output because of meta strategy tie.
output = random.choice(['R','P','S'])
################################################
# Program used to compute thresholds is below. #
################################################
# #!/usr/bin/python
# # usage: findthresholds.py [threshold as fraction of 1.0]
# import sys
# if len(sys.argv) != 2:
# print "must give exactly one command line argument with your desired threshold"
# sys.exit(1)
# limit = float(sys.argv[1])
# if limit > 1.0 or limit < 0.0:
# print "desired threshold should be given as a fraction of 1.0"
# sys.exit(1)
# bprob = [1.0] * 1000 + [0.0] * 1001
# aprob = [0.0] * 2001
# thresh = [0] * 1000
# for i in reversed(xrange(0,1000)):
# for j in xrange(0,2001):
# x = bprob[j-1] if (j != 0) else 1.0
# y = bprob[j]
# z = bprob[j+1] if (j != 2000) else 0.0
# aprob[j] = (x + y + z) / 3.0
# if aprob[j] > limit:
# thresh[i] = 1000-(j)
# tprob = bprob
# bprob = aprob
# aprob = tprob
# start = 0
# for i in xrange(1,1000):
# if thresh[i] != thresh[i-1]:
# print "Threshold of " + str(thresh[i-1]) + " from " + str(start) + " to " + str(i-1)
# start = i
# print "Threshold of " + str(thresh[len(thresh)-1]) + " from " + str(start) + " to " + str(len(thresh)-1)