def find_nash_equilibrium(score_lookup):
modified_score_lookup = remove_guaranteed_opponent_moves(score_lookup)
if not modified_score_lookup:
modified_score_lookup = score_lookup
df = pd.Series(modified_score_lookup).unstack()
equilibria = find_all_equilibria(df)
best_eq, score = find_best_nash_equilibrium(equilibria, df)
bot_percentages = best_eq[0]
opponent_percentages = best_eq[1]
bot_choices = df.index
opponent_choices = df.columns
return bot_choices, opponent_choices, bot_percentages, opponent_percentages, score
def pick_aggresive(self, score_lookup):
# Helper function that gets rid of moves that have no option
modified_score_lookup = remove_guaranteed_opponent_moves(score_lookup)
if not modified_score_lookup:
modified_score_lookup = score_lookup
worst_case = defaultdict(lambda: (tuple(), float('-inf')))
logger.debug(f"\nModified Scores: {modified_score_lookup}")
# Simply selects the highest scoring move
for move_pair, result in modified_score_lookup.items():
if worst_case[move_pair[0]][1] < result:
worst_case[move_pair[0]] = move_pair, result
aggresive = max(worst_case, key=lambda x: worst_case[x][1])
logger.debug(f"Worst case: {worst_case})")
return worst_case[aggresive]
def pick_safest(self, score_lookup):
# Helper function that gets rid of moves that have no option
modified_score_lookup = remove_guaranteed_opponent_moves(score_lookup)
if not modified_score_lookup:
modified_score_lookup = score_lookup
worst_case = defaultdict(lambda: (tuple(), float('inf')))
logger.debug(f"\nModified Scores: {modified_score_lookup}")
# Pick move that that minimizes the maxmimum loss
for move_pair, result in modified_score_lookup.items():
if worst_case[move_pair[0]][1] > result:
worst_case[move_pair[0]] = move_pair, result
safest = max(worst_case, key=lambda x: worst_case[x][1])
logger.debug(f"Worst case: {worst_case})")
return worst_case[safest]