83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
import random
|
|
import torch as T
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
from game import Game
|
|
from tqdm import tqdm
|
|
|
|
from os import environ
|
|
environ['PYGAME_HIDE_SUPPORT_PROMPT'] = '1'
|
|
|
|
|
|
random.seed(1)
|
|
np.random.seed(1)
|
|
T.manual_seed(1)
|
|
|
|
n_episodes = 10000
|
|
game_len = 5000
|
|
|
|
figure_file = 'plots/score.png'
|
|
|
|
game = Game()
|
|
|
|
agent_list = [0 for _ in range(game.max_num_players)]
|
|
|
|
score_history = np.zeros(
|
|
shape=(game.max_num_players, n_episodes))
|
|
best_score = np.zeros(game.max_num_players)
|
|
avg_score = np.zeros(game.max_num_players)
|
|
|
|
for i in tqdm(range(n_episodes)):
|
|
# TODO: Make game.level.reset_map() so we don't __init__ everything all the time (such a waste)
|
|
if i != 0:
|
|
game.level.__init__(reset=True)
|
|
# TODO: Make game.level.reset_map() so we don't pull out and load the agent every time (There is -definitevly- a better way)
|
|
for player in game.level.player_sprites:
|
|
for agent in agent_list:
|
|
player.agent = agent_list[player.player_id]
|
|
player.stats.exp = score_history[player.player_id][i-1]
|
|
|
|
agent_list = [0 for _ in range(game.max_num_players)]
|
|
|
|
for j in range(game_len):
|
|
if not game.level.done:
|
|
|
|
game.run()
|
|
game.calc_score()
|
|
|
|
for player in game.level.player_sprites:
|
|
if player.is_dead():
|
|
agent_list[player.player_id] = player.agent
|
|
player.kill()
|
|
|
|
# if (j == game_len-1 or game.level.done) and game.level.enemy_sprites != []:
|
|
# for player in game.level.player_sprites:
|
|
# for enemy in game.level.enemy_sprites:
|
|
# player.stats.exp *= .95
|
|
else:
|
|
break
|
|
|
|
for player in game.level.player_sprites:
|
|
if not player.is_dead():
|
|
agent_list[player.player_id] = player.agent
|
|
exp_points = player.stats.exp
|
|
score_history[player.player_id][i] = exp_points
|
|
avg_score[player.player_id] = np.mean(
|
|
score_history[player.player_id])
|
|
if avg_score[player.player_id] > best_score[player.player_id]:
|
|
best_score[player.player_id] = avg_score[player.player_id]
|
|
print(f"Saving models for agent {player.player_id}...")
|
|
player.agent.save_models(
|
|
actr_chkpt=f"player_{player.player_id}_actor", crtc_chkpt=f"player_{player.player_id}_critic")
|
|
print("Models saved ...\n")
|
|
|
|
print(
|
|
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]}")
|
|
|
|
|
|
plt.plot(score_history[0])
|
|
|
|
game.quit()
|
|
|
|
plt.show()
|