def train(display_on, speed, params):
pygame.init()
pygame.font.init()
agent = DQNAgent(params)
counter_games = 0
high_score = 0
score_plot = []
counter_plot = []
while counter_games < params['episodes']:
game = Game(440, 440, high_score)
if display_on:
game.update_display()
while not game.crash:
if handle_game_event(game):
return
# agent.epsilon is set to give randomness to actions
agent.epsilon = 1 - (counter_games *
params['epsilon_decay_linear'])
state = game.get_state()
move = agent.get_move(state)
game.do_move(move)
new_state = game.get_state()
reward = get_reward(game)
# train short memory base on the new action and state
agent.train_short_memory(state, move, reward, new_state,
game.crash)
agent.remember(state, move, reward, new_state, game.crash)
if display_on:
game.update_display()
pygame.time.wait(speed)
counter_games += 1
print(f'Game {counter_games} Score: {game.score}')
high_score = game.high_score
score_plot.append(game.score)
counter_plot.append(counter_games)
agent.replay_memory(params['batch_size'])
agent.model.save_weights(params['weights_path'])
pygame.quit()
plot_seaborn(counter_plot, score_plot)
def play(display_on, speed, params):
pygame.init()
pygame.font.init()
agent = DQNAgent(params)
agent.epsilon = 0
counter_games = 0
high_score = 0
score_plot = []
counter_plot = []
while counter_games < params['episodes']:
game = Game(440, 440, high_score)
if display_on:
game.update_display()
while not game.crash:
if handle_game_event(game):
return
state = game.get_state()
move = agent.get_move(state)
game.do_move(move)
if display_on:
game.update_display()
pygame.time.wait(speed)
counter_games += 1
print(f'Game {counter_games} Score: {game.score}')
high_score = game.high_score
score_plot.append(game.score)
counter_plot.append(counter_games)
pygame.quit()
plot_seaborn(counter_plot, score_plot)
