def eval():
env = Tetris()
max_steps = None
epsilon_stop_episode = 1500
mem_size = 20000
discount = 0.95
batch_size = 512
epochs = 1
replay_start_size = 2000
n_neurons = [32, 32]
render_delay = None
activations = ['relu', 'relu', 'linear']
agent = DQNAgent(env.get_state_size(),
n_neurons=n_neurons,
activations=activations,
epsilon=0,
epsilon_stop_episode=epsilon_stop_episode,
mem_size=mem_size,
discount=discount,
replay_start_size=replay_start_size,
train=False)
agent.load("ckpts/591_model.ckpt")
current_state = env.reset()
done = False
steps = 0
# 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.play(best_action[0],
best_action[1],
render=True,
render_delay=render_delay)
agent.add_to_memory(current_state, next_states[best_action], reward,
done)
current_state = next_states[best_action]
steps += 1
def dqn():
env = Tetris()
episodes = 2000
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 = []
for episode in tqdm(range(episodes)):
current_state = env.reset()
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.play(best_action[0],
best_action[1],
render=render,
render_delay=render_delay)
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)
# Save Weights
agent.save(f'ckpts/{scores[-1]}_model.ckpt')