62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from ReinforcmentLearning.game import run_game
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from ReinforcmentLearning.util import initial_q_fill
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def multipleTries(EPSILON, ALPHA, GAMMA, AMOUNT_TRIES, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE):
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cookies_per_try = []
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wins_per_try = []
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for x in range(AMOUNT_TRIES):
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cookies_per_run, amount_wins = oneTry(EPSILON, ALPHA, GAMMA, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE)
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cookies_per_run.append(cookies_per_run)
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wins_per_try.append(amount_wins)
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# print(f"Finished try {x+1}\n")
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return cookies_per_try, wins_per_try
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def oneTry(EPSILON, ALPHA, GAMMA, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE):
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"""
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state: (x_distance_to_ghost, y_distance_to_ghost, next_cookie_Direction)
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action: Direction
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q_value: (state, action)
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"""
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q_values = {}
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initial_q_fill(q_values)
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cookies_per_run = []
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# Amount of single runs
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for x in range(AMOUNT_RUNS):
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amount_cookies_ate = run_game(q_values, EPSILON, ALPHA, GAMMA, REWARD_ON_WIN, REWARD_ON_LOSE)
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cookies_per_run.append(amount_cookies_ate)
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wins = 0
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for element in cookies_per_run:
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if element == 20:
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wins += 1
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# print(f"Win percentage: {(wins/AMOUNT_RUNS)*100}%")
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return cookies_per_run, wins
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def print_results(cookies_per_try, wins_per_try, EPSILON, ALPHA, GAMMA, AMOUNT_RUNS):
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# print("---------DONE---------")
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# print("Used: ")
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# print(f"Epsilon: {EPSILON}")
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# print(f"Gamma: {GAMMA}")
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# print(f"Alpha: {ALPHA}")
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# print("---------SUMMARY---------")
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print(f"Average win percantage: {((sum(wins_per_try) / len(wins_per_try)) / AMOUNT_RUNS)*100}%\n")
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# print(f"Best try: {(max(wins_per_try) / AMOUNT_RUNS)*100}%")
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# print(f"Worst try: {(min(wins_per_try) / AMOUNT_RUNS)*100}%")
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