def dqn():
env = TetrisApp(8, 16, 750, False, 40, 30 * 100)
episodes = 5000
max_steps = None
epsilon_stop_episode = 1500
mem_size = 20000
discount = 0.95
batch_size = 512
epochs = 1
render_every = 50
log_every = 50
replay_start_size = 2000
train_every = 1
n_neurons = [32, 32]
render_delay = None
activations = ['relu', 'relu', 'linear']
agent = DQNAgent(env.get_state_size(),
n_neurons=n_neurons,
activations=activations,
epsilon_stop_episode=epsilon_stop_episode,
mem_size=mem_size,
discount=discount,
replay_start_size=replay_start_size)
# log_dir = f'logs/tetris-nn={str(n_neurons)}-mem={mem_size}-bs={batch_size}-e={epochs}-{datetime.now().strftime("%Y%m%d-%H%M%S")}'
# log = CustomTensorBoard(log_dir=log_dir)
scores = []
env.pcrun()
for episode in tqdm(range(episodes)):
env.reset()
current_state = env._get_board_props(env.board)
done = False
steps = 0
if render_every and episode % render_every == 0:
render = True
else:
render = False
# Game
while not done and (not max_steps or steps < max_steps):
next_states = env.get_next_states()
best_state = agent.best_state(next_states.values())
best_action = None
for action, state in next_states.items():
if state == best_state:
best_action = action
break
reward, done = env.pcplace(best_action[0], best_action[1])
agent.add_to_memory(current_state, next_states[best_action],
reward, done)
current_state = next_states[best_action]
steps += 1
scores.append(env.get_game_score())
# Train
if episode % train_every == 0:
agent.train(batch_size=batch_size, epochs=epochs)
# Logs
# if log_every and episode and episode % log_every == 0:
# avg_score = mean(scores[-log_every:])
# min_score = min(scores[-log_every:])
# max_score = max(scores[-log_every:])
# log.log(episode, avg_score=avg_score, min_score=min_score,
# max_score=max_score)
plt.xlabel("Episodes")
plt.ylabel('Average score over 30 episodes')
plt.grid()
plt.plot(np.linspace(30, episodes, episodes - 29),
moving_average(scores, 30))
plt.savefig("nlinker.png")
