def run(params):
"""
Run the DQN algorithm, based on the parameters previously set.
"""
pygame.init()
agent = DQNAgent(params)
agent = agent.to(DEVICE)
agent.optimizer = optim.Adam(agent.parameters(),
weight_decay=0,
lr=params['learning_rate'])
counter_games = 0
score_plot = []
counter_plot = []
record = 0
total_score = 0
while counter_games < params['episodes']:
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
quit()
# Initialize classes
game = Game(440, 440)
player1 = game.player
food1 = game.food
# Perform first move
initialize_game(player1, game, food1, agent, params['batch_size'])
if params['display']:
display(player1, food1, game, record)
while not game.crash:
if not params['train']:
agent.epsilon = 0.01
else:
# agent.epsilon is set to give randomness to actions
agent.epsilon = 1 - (counter_games *
params['epsilon_decay_linear'])
# get old state
state_old = agent.get_state(game, player1, food1)
# perform random actions based on agent.epsilon, or choose the action
if random.uniform(0, 1) < agent.epsilon:
final_move = np.eye(3)[randint(0, 2)]
else:
# predict action based on the old state
with torch.no_grad():
state_old_tensor = torch.tensor(
state_old.reshape(
(1, 11)), dtype=torch.float32).to(DEVICE)
prediction = agent(state_old_tensor)
final_move = np.eye(3)[np.argmax(
prediction.detach().cpu().numpy()[0])]
# perform new move and get new state
player1.do_move(final_move, player1.x, player1.y, game, food1,
agent)
state_new = agent.get_state(game, player1, food1)
# set reward for the new state
reward = agent.set_reward(player1, game.crash)
if params['train']:
# train short memory base on the new action and state
agent.train_short_memory(state_old, final_move, reward,
state_new, game.crash)
# store the new data into a long term memory
agent.remember(state_old, final_move, reward, state_new,
game.crash)
record = get_record(game.score, record)
if params['display']:
display(player1, food1, game, record)
pygame.time.wait(params['speed'])
if params['train']:
agent.replay_new(agent.memory, params['batch_size'])
counter_games += 1
total_score += game.score
print(f'Game {counter_games} Score: {game.score}')
score_plot.append(game.score)
counter_plot.append(counter_games)
mean, stdev = get_mean_stdev(score_plot)
if params['train']:
model_weights = agent.state_dict()
torch.save(model_weights, params["weights_path"])
if params['plot_score']:
plot_seaborn(counter_plot, score_plot, params['train'])
return total_score, mean, stdev
def run(params):
"""
Run the DQN algorithm, based on the parameters previously set.
"""
pygame.init()
agent = DQNAgent(params)
agent = agent.to(DEVICE)
agent.optimizer = optim.Adam(agent.parameters(), weight_decay=0, lr=params['learning_rate'])
counter_games = 0
score_plot = []
counter_plot = []
record = 0
total_score = 0
while counter_games < params['epoch']:
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
quit()
# Initialize classes
game = Game(440, 440)
player1 = game.player
food1 = game.food
# Pierwszy ruch
initialize_game(player1, game, food1, agent, params['batch_size'])
if params['display']:
display(player1, food1, game, record)
steps = 0 # Ruchy od ostatniej otrzymanej nagrody
while (not game.crash) and (steps < 100):
if not params['train']:
agent.epsilon = 0.01
else:
# agent.epsilon dla losowosci akcji
agent.epsilon = 1 - (counter_games * params['epsilon_decay_linear'])
# Otrzymaj stary stan
state_old = agent.get_state(game, player1, food1)
# Wykonuj losowe akcje na podstawie agent.epsilon albo na podstawie starych
if random.uniform(0, 1) < agent.epsilon:
final_move = np.eye(3)[randint(0,2)]
else:
# Predykcja na podstawie starych akcji
with torch.no_grad():
state_old_tensor = torch.tensor(state_old.reshape((1, 11)), dtype=torch.float32).to(DEVICE)
prediction = agent(state_old_tensor)
final_move = np.eye(3)[np.argmax(prediction.detach().cpu().numpy()[0])]
# Wykonaj nowy ruch i otrzymaj nowy stan
player1.do_move(final_move, player1.x, player1.y, game, food1, agent)
state_new = agent.get_state(game, player1, food1)
# Okresl nagrode dla nowego ruchu
reward = agent.set_reward(player1, game.crash)
# Kiedy zje, zeruj kroki
if reward > 0:
steps = 0
if params['train']:
# Trenuj pamiec krotka na podstawie nowych ruchow
agent.train_short_memory(state_old, final_move, reward, state_new, game.crash)
# Zapisz pamiec nowych ruchow do pamieci dlugiej
agent.remember(state_old, final_move, reward, state_new, game.crash)
record = get_record(game.score, record)
if params['display']:
display(player1, food1, game, record)
pygame.time.wait(params['speed'])
steps+=1
if params['train']:
agent.replay_new(agent.memory, params['batch_size'])
counter_games += 1
total_score += game.score
print(f'Game {counter_games} Score: {game.score}')
score_plot.append(game.score)
counter_plot.append(counter_games)
mean, stdev = get_mean_stdev(score_plot)
if params['train']:
model_weights = agent.state_dict()
torch.save(model_weights, params["weights_path"])
if params['plot_score']:
plot_seaborn(counter_plot, score_plot, params['train'])
return total_score, mean, stdev