def run_game():
env = Tetris()
episodes = 2000
max_steps = None
discount = 0.98
replay_mem_size = 20000
minibatch_size = 512
epsilon = 1
epsilon_min = 0
epsilon_stop_episode = 1500
learning_rate = 5e-3
epochs = 1
show_every = 50
log_every = 50
replay_start_size = 2000
train_every = 1
hidden_dims = [64, 64]
activations = ['relu', 'relu', 'linear']
agent = DQNAgent(env.get_state_size(), discount=discount, \
replay_mem_size=replay_mem_size, \
minibatch_size=minibatch_size, epsilon=epsilon, \
# epsilon_decay=epsilon_decay, \
epsilon_min=epsilon_min, \
epsilon_stop_episode=epsilon_stop_episode, \
learning_rate=learning_rate, hidden_dims=hidden_dims, \
activations=activations, \
replay_start_size=replay_start_size)
log_dir = f'log/tetris-{datetime.now().strftime("%Y%m%d-%H%M%S")}-nn={str(hidden_dims)}-mem={replay_mem_size}-bs={minibatch_size}-discount={discount}'
log = ModifiedTensorBoard(log_dir=log_dir)
scores = []
for episode in tqdm(range(episodes)):
current_state = env.reset_game()
done = False
step = 0
log.step = episode
if show_every and episode % show_every == 0:
show = True
else:
show = False
# Run the game until either game over or we've hit max number of steps
while not done and (not max_steps or step < max_steps):
next_states = env.get_next_states()
best_state = agent.best_state(next_states.values())
best_action = None
# action is (x,i), state is [lines_cleared, holes, total_bumpiness, sum_height]
for action, state in next_states.items():
if state == best_state:
best_action = action
break
# reward is the score, done is gameover status
reward, done = env.play_game(best_action[0],
best_action[1],
show=show)
if show:
env.show()
agent.update_replay_memory(current_state, best_action,
next_states[best_action], reward, done)
# move to next timestep
current_state = next_states[best_action]
step += 1
if show:
# After game is completed, collect the final score
print("Episode %d score: %d epsilon: %.2f" %
(episode, env.get_game_score(), agent.epsilon))
scores.append(env.get_game_score())
agent.train(epochs=epochs)
if log_every and episode % log_every == 0:
avg_score = mean(scores[-log_every:])
min_score = min(scores[-log_every:])
max_score = max(scores[-log_every:])
log.update_stats(avg_score=avg_score,
min_score=min_score,
max_score=max_score)
if env.get_game_score() >= MIN_SCORE:
if not os.path.exists('models/'):
os.makedirs('models/')
agent.model.save(
f'models/eps_{str(episode)}nn_{str(hidden_dims)}__bs{minibatch_size}__score_{env.get_game_score()}__{int(time.time())}.h5'
)
# Get action from Q table
action = np.argmax(agent.get_qs(current_state))
else:
# Get random action
action = np.random.randint(0, env.ACTION_SPACE_SIZE)
new_state, reward, done = env.step(action)
# Transform new continous state to new discrete state and count reward
episode_reward += reward
if SHOW_PREVIEW:# and not episode % AGGREGATE_STATS_EVERY:
env.render()
# Every step we update replay memory and train main network
agent.update_replay_memory((current_state, action, reward, new_state, done))
agent.train(done, step)
current_state = new_state
step += 1
# Append episode reward to a list and log stats (every given number of episodes)
ep_rewards.append(episode_reward)
if not episode % AGGREGATE_STATS_EVERY or episode == 1:
average_reward = sum(ep_rewards[-AGGREGATE_STATS_EVERY:])/len(ep_rewards[-AGGREGATE_STATS_EVERY:])
min_reward = min(ep_rewards[-AGGREGATE_STATS_EVERY:])
max_reward = max(ep_rewards[-AGGREGATE_STATS_EVERY:])
agent.tensorboard.update_stats(reward_avg=average_reward, reward_min=min_reward, reward_max=max_reward, epsilon=epsilon)
# Save model, but only when min reward is greater or equal a set value
if episode % 50 == 0:
