83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
import random
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import torch as T
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import numpy as np
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import matplotlib.pyplot as plt
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from game import Game
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from tqdm import tqdm
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from os import environ
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environ['PYGAME_HIDE_SUPPORT_PROMPT'] = '1'
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random.seed(1)
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np.random.seed(1)
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T.manual_seed(1)
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n_episodes = 10000
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game_len = 20000
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figure_file = 'plots/score.png'
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game = Game()
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agent_list = [0 for _ in range(game.max_num_players)]
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score_history = np.zeros(
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shape=(game.max_num_players, n_episodes))
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best_score = np.zeros(game.max_num_players)
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avg_score = np.zeros(game.max_num_players)
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for i in tqdm(range(n_episodes)):
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# TODO: Make game.level.reset_map() so we don't __init__ everything all the time (such a waste)
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if i != 0:
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game.level.__init__(reset=True)
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# TODO: Make game.level.reset_map() so we don't pull out and load the agent every time (There is -definitevly- a better way)
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for player in game.level.player_sprites:
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for agent in agent_list:
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player.agent = agent_list[player.player_id]
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player.stats.exp = score_history[player.player_id][i-1]
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agent_list = [0 for _ in range(game.max_num_players)]
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for j in range(game_len):
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if not game.level.done:
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game.run()
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game.calc_score()
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for player in game.level.player_sprites:
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if player.is_dead():
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agent_list[player.player_id] = player.agent
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player.kill()
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# if (j == game_len-1 or game.level.done) and game.level.enemy_sprites != []:
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# for player in game.level.player_sprites:
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# for enemy in game.level.enemy_sprites:
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# player.stats.exp *= .95
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else:
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break
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for player in game.level.player_sprites:
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if not player.is_dead():
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agent_list[player.player_id] = player.agent
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exp_points = player.stats.exp
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score_history[player.player_id][i] = exp_points
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avg_score[player.player_id] = np.mean(
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score_history[player.player_id])
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if avg_score[player.player_id] > best_score[player.player_id]:
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best_score[player.player_id] = avg_score[player.player_id]
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print(f"Saving models for agent {player.player_id}...")
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player.agent.save_models(actr_chkpt = f"player_{player.player_id}_actor", crtc_chkpt = f"player_{player.player_id}_critic")
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print("Models saved ...\n")
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print(
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f"\nCumulative score for player {player.player_id}: {score_history[0][i]}\nAverage score for player {player.player_id}: {avg_score[player.player_id]}\nBest score for player {player.player_id}: {best_score[player.player_id]}")
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plt.plot(score_history[0])
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game.quit()
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plt.show()
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