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RockPaperScissorsLizardSpoke_2.py
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RockPaperScissorsLizardSpoke_2.py
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from enum import Enum
from random import choices
from typing import List
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import time
class Action(Enum):
ROCK = 0
PAPER = 1
SCISSORS = 2
SPOKE = 3
LIZARD = 4
cumulative_regrets = np.zeros(shape=(len(Action)), dtype=int)
strategy_sum = np.zeros(shape=(len(Action)))
opp_strategy = [0.2, 0.1, 0.2, 0.1, 0.4]
# opp_strategy = random.choices(list(Action), weights=strategy)[0]
def get_strategy(cumulative_regrets: np.array) -> np.array:
"""Return regret-matching strategy"""
pos_cumulative_regrets = np.maximum(0, cumulative_regrets)
if sum(pos_cumulative_regrets) > 0:
return pos_cumulative_regrets / sum(pos_cumulative_regrets)
else:
return np.full(shape=len(Action), fill_value=1/len(Action))
def get_payoff(action_1: Action, action_2: Action) -> int:
"""Returns the payoff for player 1"""
mod3_val = (action_1.value - action_2.value) % 5
if mod3_val == 2:
return -1
elif mod3_val == 3:
return 1
elif mod3_val == 4:
return -1
else:
return mod3_val
def get_regrets(payoff: int, action_2: Action) -> List[int]:
"""return regrets"""
return np.array([get_payoff(a, action_2) - payoff for a in Action])
cumulative_regrets = np.zeros(shape=(len(Action)), dtype=int)
strategy_sum = np.zeros(shape=(len(Action)))
num_iterations = 20000
for i in range(num_iterations):
# compute the strategy according to regret matching
strategy = get_strategy(cumulative_regrets)
# add the strategy to our running total of strategy probabilities
strategy_sum += strategy
# Choose our action and our opponent's action
our_action = random.choices(list(Action), weights=strategy)[0]
opp_action = random.choices(list(Action), weights=strategy)[0]
# compute the payoff and regrets
our_payoff = get_payoff(our_action, opp_action)
regrets = get_regrets(our_payoff, opp_action)
# add regrets from this round to the cumulative regrets
cumulative_regrets += regrets
optimal_strategy_graph = strategy_sum / i