def main():
#env = gym_super_mario_bros.make('SuperMarioBros-v0')
env = gym_super_mario_bros.make('SuperMarioBros-1-1-v1')
env = BinarySpaceToDiscreteSpaceEnv(env, SIMPLE_MOVEMENT)
timestart = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d-%H:%M:%S')
# env = VideoRecorderWrapper(env, PROJ_DIR + "/../video", str(timestart), 50)
env = VideoRecorderWrapper(env, PROJ_DIR + "/../video/final", str(timestart), 1)
env = DownsampleEnv(env, (84, 84))
env = PenalizeDeathEnv(env, penalty=-25)
env = FrameStackEnv(env, 4)
# good
#act = deepq.load(PROJ_DIR+"/../models/mario_model_2018-08-12-13:00:58.pkl")
# better
act = deepq.load(PROJ_DIR + "/../models/mario_model_2018-08-12-19:21:50.pkl")
episode = 0
while True:
obs, done = env.reset(), False
stepnr = 0
episode_rew = 0
while not done:
env.render()
obs, rew, done, _ = env.step(act(obs[None])[0])
if stepnr % 20 == 0:
plot_obs(obs)
episode_rew += rew
stepnr += 1
print("Episode reward", episode_rew, episode)
episode = episode+1
def run(self, solution, level, render, mode): env = gym_super_mario_bros.make(level) env = BinarySpaceToDiscreteSpaceEnv(env, COMPLEX_MOVEMENT) done = True reason_finish = "no_more_commands" pos = 0 total_r = 0 for step in range(len(solution)): if done: state = env.reset() state, reward, done, info = env.step(solution[pos]) pos+=1 if reward == -15: #faleceu reason_finish = "death" break if mode == "level" and info['flag_get'] == True: reason_finish = "win" break total_r = total_r + reward if render == "true": env.render() env.close() return total_r, pos, info, reason_finish
def main(path="./models/deepq/mario_reward_1736.7.pkl"):
step_mul = 16
steps = 200
FLAGS = flags.FLAGS
flags.DEFINE_string("env", "SuperMarioBros-v0", "RL environment to train.")
flags.DEFINE_string("algorithm", "deepq", "RL algorithm to use.")
FLAGS(sys.argv)
# 1. Create gym environment
env = gym_super_mario_bros.make('SuperMarioBros-v0')
env = BinarySpaceToDiscreteSpaceEnv(env, SIMPLE_MOVEMENT)
act = deepq.load(path)
nstack = 4
nh, nw, nc = env.observation_space.shape
history = np.zeros((1, nh, nw, nc * nstack), dtype=np.uint8)
obs, done = env.reset(), False
# history = update_history(history, obs)
episode_rew = 0
while not done:
env.render()
action = act([obs])[0]
obs, rew, done, _ = env.step(action)
# history = update_history(history, obs)
episode_rew += rew
print("action : %s reward : %s" % (action, rew))
print("Episode reward", episode_rew)
class Environment:
actionMap = {
0: 'NOOP',
1: 'Right',
2: 'Right-Jump',
3: 'Right-Sprint',
4: 'Right-Jump-Sprint',
5: 'Jump',
6: 'Left'
}
def __init__(self, rows=19, columns=16, verbose=True, raw=True, variant=1):
self.verbose = verbose
self.raw = raw
self.variant = variant
self.img2state = Img2State(rows=19, columns=16)
self.game = BinarySpaceToDiscreteSpaceEnv(
gym_super_mario_bros.make('SuperMarioBros-v3'), SIMPLE_MOVEMENT)
self.state = self.img2state.transfrom(self.game.reset(),
raw=self.raw,
variant=self.variant)
self.reward = 0
# Actions
self.A = list(Environment.actionMap.keys())
def step(self, action: int):
if action not in self.A:
raise Exception('Wrong Action...')
state, self.reward, done, info = self.game.step(action)
self.state = self.img2state.transfrom(state,
raw=self.raw,
variant=self.variant)
if done and self.state[8]:
self.reward = 100
elif self.state[8]:
self.reward = 30
elif self.state[9]:
self.reward = 15
if self.verbose:
self.game.render()
return done
def reset(self):
self.state = self.img2state.transfrom(self.game.reset(),
raw=self.raw,
variant=self.variant)
self.reward = 0
class Environment(threading.Thread):
stop_signal = False
def __init__(self,
render=False,
eps_start=EPS_START,
eps_end=EPS_STOP,
eps_steps=EPS_STEPS):
threading.Thread.__init__(self)
self.render = render
# Make the super mario gym environment and apply wrappers
self.env = gym.make(ENV)
self.env = BinarySpaceToDiscreteSpaceEnv(self.env, SIMPLE_MOVEMENT)
self.env = preprocess.GrayScaleImage(self.env,
height=HIGHT,
width=WIDTH,
grayscale=True)
# self.env = wrappers.Monitor(self.env, "./Super_Mario_AI/videos", force = True, write_upon_reset=True)
self.agent = Agent(TEMPERATURE)
def runEpisode(self):
s = self.env.reset()
R = 0
while True:
time.sleep(THREAD_DELAY) # yield
if self.render: self.env.render()
a = self.agent.act(s)
s_, r, done, info = self.env.step(a)
if done: # terminal state
s_ = None
self.agent.train(s, a, r, s_)
s = s_
R += r
if done or self.stop_signal:
break
print("Total R:", R)
def run(self):
while not self.stop_signal:
self.runEpisode()
def stop(self):
self.stop_signal = True
class MarioEnv:
def __init__(self, os='mac', display=False):
self.display = display
if os == 'mac' or os == 'linux':
env = gym_super_mario_bros.make('SuperMarioBros-v0')
self.env = BinarySpaceToDiscreteSpaceEnv(env, SIMPLE_MOVEMENT)
else:
raise Exception("bad os")
self.act_dim = self.env.action_space.n
self.obs_dim = (1, 128, 128)
print("env created with act_dim", self.act_dim, "obs_dim",
self.obs_dim)
self.transform = transforms.Compose([
transforms.ToTensor(), # chain 2 transforms together using list.
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
def reset(self):
state = self.env.reset()
return self.__resize_image(state)
def step(self, action):
state, reward, done, info = self.env.step(action)
if reward == 0:
reward = -0.5
state_t = self.__resize_image(state)
return state_t, \
np.reshape(reward, -1), \
np.reshape(done, -1)
def close(self):
self.env.close()
def __resize_image(self, state):
state_new = cv2.resize(state, (128, 128))
img = Image.fromarray(state_new)
state_t = self.transform(img)[0, :, :].unsqueeze(0)
state_t = state_t.float().to(DEVICE)
return state_t.unsqueeze(0)
def render(self):
if self.display:
self.env.render()
def main():
env = gym_super_mario_bros.make('SuperMarioBros-v0')
env = BinarySpaceToDiscreteSpaceEnv(env, SIMPLE_MOVEMENT)
done = True
max_step = 5000
print(env.observation_space.shape)
#win下加ascii=True才会不换行
qbar = tqdm(max_step, ascii=True)
for step in range(max_step):
qbar.update()
if done:
state = env.reset()
action = get_action(state, env.action_space)
state, reward, done, info = env.step(action)
if done:
print(str(step) + " 英雄请卷土重来" + str(info))
env.render()
env.close()
qbar.close()
class MarioEnv(Process):
def __init__(self,
env_id,
idx,
child_conn,
queue,
n_step,
is_render=False):
super(MarioEnv, self).__init__()
self.idx = idx
self.env_id = env_id
self.child_conn = child_conn
self.queue = queue
self.is_render = is_render
self.n_step = n_step
self.steps = 0
self.episodes = 0
self.accum_reward = 0
self.transition = []
def run(self):
super(MarioEnv, self).run()
self.env = gym_super_mario_bros.make(self.env_id)
self.env = BinarySpaceToDiscreteSpaceEnv(self.env, SIMPLE_MOVEMENT)
self.reset()
print('[ Worker %2d ] ' % (self.idx), end='')
print('Playing <', self.env_id, '>')
self.request_action(0, False)
while True:
action = self.child_conn.recv()
next_state, reward, done, info = self.env.step(action)
self.steps += 1
self.accum_reward += reward
next_state = rgb2dataset(next_state)
if self.is_render and self.idx == 0:
self.env.render()
# make a transition
self.transition.append(next_state)
if len(self.transition) > 4:
self.transition.pop(0)
if done:
self.send_result(info['x_pos'])
self.reset()
self.request_action(reward, True)
else:
self.request_action(reward, False)
def reset(self):
state = self.env.reset()
state = rgb2dataset(state)
self.transition.clear()
self.transition.append(state)
self.steps = 0
self.episodes += 1
self.accum_reward = 0
def request_action(self, reward, done):
self.queue.put([self.idx, "OnStep", [self.transition, reward, done]])
def send_result(self, x_pos):
self.queue.put([
self.idx, "Result",
[self.episodes, self.steps, self.accum_reward, x_pos]
])
def main():
movement = SIMPLE_MOVEMENT
movement.append(['left', 'A'])
movement.append(['left', 'B'])
movement.append(['left', 'A', 'B'])
movement.append(['B'])
movement.append(['down'])
movement.append(['up'])
env = gym_super_mario_bros.make('SuperMarioBros-1-1-v0')
env = BinarySpaceToDiscreteSpaceEnv(env, movement)
#channels is acting as the number of frames in history
#if resize_height and height are different, assert final_height < resize_height and image will be cropped
channels = 4
width = 84
resize_height = 110
final_height = 84
size = [channels, final_height, width]
batch_size = 32
replay_capacity = 100000
replay_dir = '/home/hansencb/mario_replay/'
epsilon = 1
gamma = 0.9
use_cuda = torch.cuda.is_available()
torch.manual_seed(1)
device = torch.device("cuda" if use_cuda else "cpu")
model = simple_net(channels, len(movement), device).to(device)
target_model = simple_net(channels, len(movement), device).to(device)
model_file = 'mario_agent'
model.load_state_dict(torch.load(model_file))
target_model.load_state_dict(torch.load(model_file))
lr = 0.001
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
total_reward_file = 'total_reward.txt'
with open(total_reward_file, 'w') as f:
f.write('Reward\tSteps\n')
max_steps = 5000
num_eps = 1000
data = dataset(replay_capacity, batch_size, replay_dir, 1, size)
for episode in range(num_eps):
print('Episode {}'.format(episode + 1))
state = env.reset()
state = preprocess(state, [resize_height, width], final_height)
state = torch.cat((state, state, state, state))
action = 0
episode_reward = 0
for step in range(max_steps):
if step % 3 == 0:
if random.random() < epsilon:
action = random.randint(0, len(movement) - 1)
else:
q_val, action = maxQ(state, model, device)
next_state, reward, done, info = env.step(int(action))
if reward > 0:
reward = 1
else:
reward = -1
episode_reward += reward
next_state = preprocess(next_state, [resize_height, width],
final_height)
next_state = torch.cat((state[1:, :, :], next_state))
trans = transition(state, action, reward, next_state, done)
data.add(trans)
train(model, device, optimizer,
data.get_batch(model, device, gamma))
state = next_state
env.render()
#time.sleep(0.03)
if done:
with open(total_reward_file, 'a') as f:
f.write('{}\t{}\n'.format(episode_reward, step))
break
epsilon -= (1 / num_eps)
if episode % 10 == 0:
target_model.load_state_dict(model.state_dict())
with open(model_file, 'wb') as f:
torch.save(model.state_dict(), f)
env.close()
class Simulator:
def __init__(self, movements, max_steps):
"""
Creates a new Simulator.
The Simulator lets individuals play the game and assigns their resulting fitness to them.
:param movements: a list of movements the individuals are allowed to make
:param max_steps: the maximum number of simulation steps an individual is allowed to use
"""
self.movements = movements
self.max_steps = max_steps
# TODO maybe another name on "env_expanded"?
self.env_expanded = gym_super_mario_bros.SuperMarioBrosEnv(
frames_per_step=1, rom_mode='vanilla')
self.env = BinarySpaceToDiscreteSpaceEnv(self.env_expanded,
self.movements)
# self.env.metadata['video.frames_per_second'] = 120
# self.env_expanded.metadata['video.frames_per_second'] = 120
self._log = logging.getLogger('MLProject.Simulator')
def _simulate_individual(self, individual: Individual, render):
"""
Simulates a single individual and assigns its fitness score.
This involves letting the individual play a game of Mario,
and assigning the resulting fitness to the individual.
:param individual:
"""
state = self.env.reset()
x_pos = 0
last_x_pos = 0
reward_final = 0
accumulated_fitness = 0
died = False
last_fps_time = time.time()
frames = 0
steps_standing_still = 0
number_of_steps_standing_still_before_kill = 200
for step in range(self.max_steps):
self.state_downscaled = get_sensor_map(self.env_expanded)
action = individual.agent.act(self.state_downscaled)
# print('\r', _vectofixedstr(action, 12), end=' ')
action = np.argmax(action)
state, reward, done, info = self.env.step(action)
if info['flag_get']:
accumulated_fitness += x_pos
x_pos = info['x_pos'] + accumulated_fitness
reward_final += reward
# Checks if reward is 0 to see if Mario stood still in the last step
if last_x_pos - 1 <= x_pos <= last_x_pos + 1:
steps_standing_still += 1
if steps_standing_still >= number_of_steps_standing_still_before_kill:
break
else:
steps_standing_still = 0
last_x_pos = x_pos
if render:
self.env.render()
if info["life"] <= 2:
died = True
break
# now = time.time()
frames += 1
"""
if now - last_fps_time >= 1:
fps = frames / (now - last_fps_time)
self._log.debug('FPS: {}'.format(fps))
last_fps_time = now
frames = 0
"""
fps = frames / (time.time() - last_fps_time)
self._log.debug('Steps per second: {:.2f}'.format(fps))
individual.fitness = x_pos
# individual.fitness = reward_final
if died:
self._log.debug(
'Individual {} died. It achieved fitness {}'.format(
individual.id, individual.fitness))
else:
self._log.debug(
'Individual {} ran out of simulation steps. It achieved fitness {}'
.format(individual.id, individual.fitness))
def simulate_generation(self, generation: Generation, render=True):
"""
Simulates the whole generation and assigns each individual a fitness score.
:param generation:
:param render:
"""
for individual in generation.individuals:
self._simulate_individual(individual, render)
def shutdown(self):
"""
Does nothing. Needed for compatibility with ParallelSimulator
"""
pass
print("Made save path at: {}".format(save_dir))
save_path = save_dir / AGENT_FILENAME
if Path.is_file(save_path):
print("Loading saved agent...")
agent.load(save_path)
done = False
batch_size = 32
for e in range(1, EPISODES + 1):
state = env.reset()
state = np.reshape(state, [1, state_size])
time = 0
while True:
env.render()
action = agent.act(state)
next_state, reward, done, _ = env.step(action)
reward = reward if not done else -10
next_state = np.reshape(next_state, [1, state_size])
agent.remember(state, action, reward, next_state, done)
state = next_state
if done or time >= 500:
print("episode: {}/{}, score: {}, e: {:.2}".format(
e, EPISODES, time, agent.epsilon))
break
time += 1
if len(agent.memory) > batch_size:
agent.replay(batch_size)
if e % 10 == 0:
agent.save(save_path)
class MarioEnvironment(Process):
def __init__(self,
env_id,
is_render,
env_idx,
child_conn,
history_size=4,
h=84,
w=84):
super(MarioEnvironment, self).__init__()
self.daemon = True
self.env = BinarySpaceToDiscreteSpaceEnv(
gym_super_mario_bros.make(env_id), movement)
self.is_render = is_render
self.env_idx = env_idx
self.steps = 0
self.episode = 0
self.rall = 0
self.recent_rlist = deque(maxlen=100)
self.child_conn = child_conn
self.history_size = history_size
self.history = np.zeros([history_size, h, w])
self.h = h
self.w = w
self.reset()
def run(self):
super(MarioEnvironment, self).run()
while True:
action = self.child_conn.recv()
if self.is_render:
self.env.render()
obs, reward, done, info = self.env.step(action)
if life_done:
# when Mario loses life, changes the state to the terminal
# state.
if self.lives > info['life'] and info['life'] > 0:
force_done = True
self.lives = info['life']
else:
force_done = done
self.lives = info['life']
else:
# normal terminal state
force_done = done
# reward range -15 ~ 15
log_reward = reward / 15
self.rall += log_reward
r = log_reward
self.history[:3, :, :] = self.history[1:, :, :]
self.history[3, :, :] = self.pre_proc(obs)
self.steps += 1
if done:
self.recent_rlist.append(self.rall)
print(
"[Episode {}({})] Step: {} Reward: {} Recent Reward: {} Stage: {} current x:{} max x:{}"
.format(self.episode, self.env_idx, self.steps, self.rall,
np.mean(self.recent_rlist), info['stage'],
info['x_pos'], self.max_pos))
self.history = self.reset()
else:
self.child_conn.send(
[self.history[:, :, :], r, False, done, log_reward])
def reset(self):
self.steps = 0
self.episode += 1
self.rall = 0
self.lives = 3
self.stage = 1
self.max_pos = 0
self.get_init_state(self.env.reset())
return self.history[:, :, :]
def pre_proc(self, X):
# grayscaling
x = cv2.cvtColor(X, cv2.COLOR_RGB2GRAY)
# resize
x = cv2.resize(x, (self.h, self.w))
x = np.float32(x) * (1.0 / 255.0)
return x
def get_init_state(self, s):
for i in range(self.history_size):
self.history[i, :, :] = self.pre_proc(s)
class MarioEnvironment(Process):
def __init__(
self,
env_id,
is_render,
env_idx,
child_conn,
history_size=4,
life_done=False,
h=84,
w=84, movement=COMPLEX_MOVEMENT, sticky_action=True,
p=0.25):
super(MarioEnvironment, self).__init__()
self.daemon = True
self.env = BinarySpaceToDiscreteSpaceEnv(
gym_super_mario_bros.make(env_id), COMPLEX_MOVEMENT)
self.is_render = is_render
self.env_idx = env_idx
self.steps = 0
self.episode = 0
self.rall = 0
self.recent_rlist = deque(maxlen=100)
self.child_conn = child_conn
self.life_done = life_done
self.sticky_action = sticky_action
self.last_action = 0
self.p = p
self.history_size = history_size
self.history = np.zeros([history_size, h, w])
self.h = h
self.w = w
self.reset()
def run(self):
super(MarioEnvironment, self).run()
while True:
action = self.child_conn.recv()
if self.is_render:
self.env.render()
# sticky action
if self.sticky_action:
if np.random.rand() <= self.p:
action = self.last_action
self.last_action = action
# 4 frame skip
reward = 0.0
done = None
for i in range(4):
obs, r, done, info = self.env.step(action)
if self.is_render:
self.env.render()
reward += r
if done:
break
# when Mario loses life, changes the state to the terminal
# state.
if self.life_done:
if self.lives > info['life'] and info['life'] > 0:
force_done = True
self.lives = info['life']
else:
force_done = done
self.lives = info['life']
else:
force_done = done
# reward range -15 ~ 15
log_reward = reward / 15
self.rall += log_reward
r = int(info.get('flag_get', False))
self.history[:3, :, :] = self.history[1:, :, :]
self.history[3, :, :] = self.pre_proc(obs)
self.steps += 1
if done:
self.recent_rlist.append(self.rall)
print(
"[Episode {}({})] Step: {} Reward: {} Recent Reward: {} Stage: {} current x:{} max x:{}".format(
self.episode,
self.env_idx,
self.steps,
self.rall,
np.mean(
self.recent_rlist),
info['stage'],
info['x_pos'],
self.max_pos))
self.history = self.reset()
self.child_conn.send([self.history[:, :, :], r, force_done, done, log_reward])
def reset(self):
self.last_action = 0
self.steps = 0
self.episode += 1
self.rall = 0
self.lives = 3
self.stage = 1
self.max_pos = 0
self.get_init_state(self.env.reset())
return self.history[:, :, :]
def pre_proc(self, X):
# grayscaling
x = cv2.cvtColor(X, cv2.COLOR_RGB2GRAY)
# resize
x = cv2.resize(x, (self.h, self.w))
return x
def get_init_state(self, s):
for i in range(self.history_size):
self.history[i, :, :] = self.pre_proc(s)
n_y=env.action_space.n,
learning_rate=0.01,
reward_decay=0.99)
for episodes in range(EPISODES):
observation = env.reset()
observation = np.array(observation).reshape(1, 240, 256, 3)
episode_reward = 0
print("episode", episodes)
while True:
if RENDER_ENV: env.render()
action = PG.choose_action(observation)
next_state, reward, done, info = env.step(action)
PG.store_transition(next_state, action, reward)
episode_rewards_sum = sum(PG.episode_rewards)
if done:
episode_rewards_sum = sum(PG.episode_rewards)
rewards.append(episode_rewards_sum)
print(episode_rewards_sum)
max_reward_so_far = np.amax(rewards)
from nes_py.wrappers import BinarySpaceToDiscreteSpaceEnv
import gym_super_mario_bros
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT
env = gym_super_mario_bros.make('SuperMarioBros-1-1-v0')
env = BinarySpaceToDiscreteSpaceEnv(env, SIMPLE_MOVEMENT)
print(env.observation_space)
print(env.get_action_meanings())
print(env.get_keys_to_action())
done = True
totalReward = 0
maxReward = 0
for step in range(1000):
if done:
state = env.reset()
action = env.action_space.sample()
state, reward, done, info = env.step(action)
#print(f"State: {state.shape} {state}")
#print(f"Info: {info}")
totalReward += reward
if totalReward > maxReward:
maxReward = totalReward
print(f"{step} ({400 - info['time']}s): {action} -> {reward} (total: {totalReward}, max: {maxReward})")
env.render(mode='human')
env.close()
class MarioEnv(Process):
def __init__(self,
env_id,
idx,
child_conn,
queue,
s_dim,
a_dim,
g_net,
g_opt,
update_iter=10,
is_render=False,
use_cuda=False):
super(MarioEnv, self).__init__()
self.idx = idx
self.env_id = env_id
self.child_conn = child_conn
self.queue = queue
self.is_render = is_render
# self.n_step = n_step
self.update_iter = update_iter
self.steps = 0
self.episodes = 0
self.accum_reward = 0
self.transition = []
self.use_cuda = use_cuda
self.device = torch.device("cuda:0" if use_cuda else "cpu")
self.s_dim = s_dim
self.a_dim = a_dim
self.g_net = g_net
self.g_opt = g_opt
self.buffer_state = []
self.buffer_action = []
self.buffer_reward = []
def run(self):
super(MarioEnv, self).run()
self.model = A3C(
self.s_dim,
self.a_dim,
gamma=0.95,
epsilon_start=1.0,
epsilon_end=0.1,
epsilon_length=100000,
use_cuda=self.use_cuda,
)
self.model.l_net.load_state_dict(self.g_net.state_dict())
self.env = gym_super_mario_bros.make(self.env_id)
self.env = BinarySpaceToDiscreteSpaceEnv(self.env, SIMPLE_MOVEMENT)
self.reset()
print('[ Worker %2d ] ' % (self.idx), end='')
print('Playing <', self.env_id, '>')
while True:
if len(self.transition) != 4:
action = self.model.get_action(self.transition, is_random=True)
else:
action = self.model.get_action(self.transition,
is_random=False)
next_state, reward, done, info = self.env.step(action)
self.steps += 1
self.accum_reward += reward
next_state = rgb2dataset(next_state)
if self.is_render and self.idx == 0:
self.env.render()
self.buffer_state.append(self.transition)
self.buffer_action.append(action)
self.buffer_reward.append(reward)
if len(self.buffer_state
) > 0 and self.steps % self.update_iter == 0:
next_transition = self.transition[1:]
next_transition.append(next_state)
self.train(next_transition, done)
self.buffer_state.clear()
self.buffer_action.clear()
self.buffer_reward.clear()
# make a transition
self.transition.append(next_state)
if len(self.transition) > 4:
self.transition.pop(0)
if done:
self.send_result(info['x_pos'])
self.reset()
def reset(self):
state = self.env.reset()
state = rgb2dataset(state)
self.transition.clear()
self.transition.append(state)
self.steps = 0
self.episodes += 1
self.accum_reward = 0
def send_result(self, x_pos):
self.queue.put([
self.idx, "Result",
[self.episodes, self.steps, self.accum_reward, x_pos]
])
def train(self, next_transition, done):
if done:
v_s_ = 0.
else:
_, v = self.model.l_net.forward(
torch.Tensor([next_transition]).to(self.device))
v_s_ = v.cpu().detach().numpy()[0][0]
prob, v = self.model.l_net.forward(
torch.Tensor(self.buffer_state).to(self.device))
buffer_v_target = []
for r in self.buffer_reward[::-1]:
v_s_ = r + self.model.gamma * v_s_
buffer_v_target.append(v_s_)
buffer_v_target.reverse()
buffer_v_target = torch.Tensor(np.array(buffer_v_target)).to(
self.device)
buffer_action = torch.Tensor(np.array(self.buffer_action)).to(
self.device)
# LOSS 함수 구성
td_error = buffer_v_target - v
loss_critic = td_error.pow(2)
dist = torch.distributions.Categorical(prob)
loss_actor = -dist.log_prob(buffer_action) * td_error.detach()
loss = (loss_critic + loss_actor).mean()
self.g_opt.zero_grad()
loss.backward()
for lp, gp in zip(self.model.l_net.parameters(),
self.g_net.parameters()):
gp._grad = lp.grad.clone().cpu()
self.g_opt.step()
self.model.l_net.load_state_dict(self.g_net.state_dict())
class MoMarioEnv(Process):
def __init__(self, args, env_idx, child_conn, history_size=4, h=84, w=84):
super(MoMarioEnv, self).__init__()
self.daemon = True
self.env = BinarySpaceToDiscreteSpaceEnv(
gym_super_mario_bros.make(args.env_id), SIMPLE_MOVEMENT)
self.is_render = args.render
self.env_idx = env_idx
self.steps = 0
self.episode = 0
self.rall = 0
self.coin = 0
self.x_pos = 0
self.time = 0
self.score = 0
self.n_mo = 5
self.morall = np.zeros(self.n_mo)
self.recent_rlist = deque(maxlen=100)
self.recent_morlist = deque(maxlen=100)
self.child_conn = child_conn
self.life_done = args.life_done
self.single_stage = args.single_stage
self.stage_bonus = 0
self.history_size = history_size
self.history = np.zeros([history_size, h, w])
self.h = h
self.w = w
self.reset()
def run(self):
super(MoMarioEnv, self).run()
while True:
action = self.child_conn.recv()
if self.is_render:
self.env.render()
obs, reward, done, info = self.env.step(action)
if self.single_stage and info["flag_get"]:
self.stage_bonus = 10000
done = True
''' Construct Multi-Objective Reward''' #####################################
# [x_pos, time, death, coin]
moreward = []
# 1. x position
xpos_r = info["x_pos"] - self.x_pos
self.x_pos = info["x_pos"]
# resolve an issue where after death the x position resets
if xpos_r < -5:
xpos_r = 0
moreward.append(xpos_r)
# 2. time penaltiy
time_r = info["time"] - self.time
self.time = info["time"]
# time is aways decreasing
if time_r > 0:
time_r = 0
moreward.append(time_r)
# 3. death
if self.lives > info['life']:
death_r = -25
else:
death_r = 0
moreward.append(death_r)
# 4. coin
coin_r = (info['coins'] - self.coin) * 100
self.coin = info['coins']
moreward.append(coin_r)
# 5. enemy
enemy_r = info['score'] - self.score
if coin_r > 0 or done:
enemy_r = 0
self.score = info['score']
moreward.append(enemy_r)
############################################################################
if self.life_done:
# when Mario loses life, changes the state to the terminal
# state.
if self.lives > info['life'] and info['life'] > 0:
force_done = True
self.lives = info['life']
else:
force_done = done
self.lives = info['life']
else:
# normal terminal state
force_done = done
# reward range -15 ~ 15
r = reward / 15
self.rall += reward
self.morall += np.array(moreward)
mor = np.array(moreward) * self.n_mo / 15
self.history[:3, :, :] = self.history[1:, :, :]
self.history[3, :, :] = self.pre_proc(obs)
self.steps += 1
score = info['score'] + self.stage_bonus
if done:
self.recent_rlist.append(self.rall)
self.recent_morlist.append(self.morall)
print(
"[Episode {}({})]\tStep: {}\tScore: {}\tMoReward: {}\tRecent MoReward: {}\tcoin: {}\tcurrent x:{}"
.format(self.episode, self.env_idx, self.steps,
score, self.morall,
np.mean(self.recent_morlist,
axis=0), info['coins'], info['x_pos']))
self.history = self.reset()
self.child_conn.send(
[self.history[:, :, :], r, force_done, done, mor, score])
def reset(self):
self.steps = 0
self.episode += 1
self.rall = 0
self.lives = 3
self.coin = 0
self.x_pos = 0
self.time = 0
self.score = 0
self.stage_bonus = 0
self.morall = np.zeros(self.n_mo)
self.get_init_state(self.env.reset())
return self.history[:, :, :]
def pre_proc(self, X):
# grayscaling
x = cv2.cvtColor(X, cv2.COLOR_RGB2GRAY)
# resize
x = cv2.resize(x, (self.h, self.w))
x = np.float32(x) * (1.0 / 255.0)
return x
def get_init_state(self, s):
for i in range(self.history_size):
self.history[i, :, :] = self.pre_proc(s)
def main():
movement = SIMPLE_MOVEMENT
movement.append(['left', 'A'])
movement.append(['left', 'B'])
movement.append(['left', 'A', 'B'])
#movement.append(['B'])
#movement.append(['down'])
#movement.append(['up'])
env = gym_super_mario_bros.make('SuperMarioBros-1-1-v0')
env = BinarySpaceToDiscreteSpaceEnv(env, movement)
#channels is acting as the number of frames in history
#if resize_height and height are different, assert final_height < resize_height and image will be cropped
channels = 3
frames = 4
width = 128
resize_height = 180
final_height = 128
bottom_chop = 15
size = [channels * frames, final_height, width]
batch_size = 16
replay_capacity = 100000
replay_dir = '/home-local/bayrakrg/mario_replay/'
start_epsilon = 1.0
stop_epsilon = 0.01
epsilon_decay = 0.00005
gamma = 0.75
use_cuda = torch.cuda.is_available()
torch.manual_seed(1)
device = torch.device("cuda" if use_cuda else "cpu")
model = simple_net(channels, len(movement), device).to(device)
target_model = simple_net(channels, len(movement), device).to(device)
model_file = 'mario_agent'
model.load_state_dict(torch.load(model_file))
target_model.load_state_dict(torch.load(model_file))
lr = 0.0001
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
total_reward_file = 'total_reward.txt'
with open(total_reward_file, 'w') as f:
f.write('Reward\tSteps\n')
max_steps = 500
num_eps = 10000
data = dataset(replay_capacity, batch_size, replay_dir, 1, size)
tau = 0
max_tau = 10000
decay_step = 0
for episode in range(num_eps):
print('Episode {}'.format(episode + 1))
state = env.reset()
state = preprocess(state, [resize_height, width, 3], final_height,
bottom_chop)
state = torch.cat((state, state, state, state))
action = 0
episode_reward = 0
for step in range(max_steps):
tau += 1
decay_step += 1
epsilon = stop_epsilon + (start_epsilon - stop_epsilon) * np.exp(
-epsilon_decay * decay_step)
if random.random() < epsilon:
action = random.randint(0, len(movement) - 1)
else:
q_val, action, q_vals = maxQ(state, model, device)
next_state, reward, done, info = env.step(int(action))
if step == max_steps - 1:
reward -= 10
if reward > 0:
reward = 1
else:
reward = -1
episode_reward += reward
next_state = preprocess(next_state, [resize_height, width, 3],
final_height, bottom_chop)
next_state = torch.cat((state[3:, :, :], next_state))
trans = transition(state, action, reward, next_state, done)
data.add(trans)
train(model, device, optimizer,
data.get_batch(model, target_model, device, gamma))
state = next_state
env.render()
if tau > max_tau:
target_model.load_state_dict(model.state_dict())
tau = 0
if done:
break
with open(total_reward_file, 'a') as f:
f.write('{}\t{}\n'.format(episode_reward, step))
if episode % 5 == 0:
with open(model_file, 'wb') as f:
torch.save(model.state_dict(), f)
env.close()
def main():
movement = SIMPLE_MOVEMENT
movement.append(['left', 'A'])
movement.append(['left', 'B'])
movement.append(['left', 'A', 'B'])
#movement.append(['B'])
#movement.append(['down'])
#movement.append(['up'])
env = gym_super_mario_bros.make('SuperMarioBros-1-1-v0')
env = BinarySpaceToDiscreteSpaceEnv(env, movement)
#channels is acting as the number of frames in history
#if resize_height and height are different, assert final_height < resize_height and image will be cropped
channels = 4
# width = 84
# resize_height = 110
# final_height = 84
width=128
resize_height = 168
final_height = 128
size = [channels, final_height, width]
batch_size = 16
replay_capacity = 100000
replay_dir = '/home/hansencb/mario_replay/'
gamma = 0.95
start_epsilon = 0.3
stop_epsilon = 0.01
epsilon_decay = 0.00025
use_cuda = torch.cuda.is_available()
torch.manual_seed(1)
device = torch.device("cuda" if use_cuda else "cpu")
model = simple_net(channels, len(movement), device).to(device)
target_model = simple_net(channels, len(movement), device).to(device)
data_file = 'data_loader'
model_file = 'mario_agent'
continue_train = True
model.load_state_dict(torch.load(model_file))
if continue_train:
target_model.load_state_dict(torch.load(model_file))
lr = 0.00005
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
total_reward_file ='total_reward.txt'
if not continue_train:
with open(total_reward_file, 'w') as f:
f.write('Reward\tSteps\n')
max_steps = 5000
num_eps = 5000
if continue_train:
with open(data_file, 'rb') as f:
data = pickle.load(f)
data.batch_size = batch_size
else:
data = dataset(replay_capacity, batch_size, replay_dir, size)
#initialize memory with 100 experiences
done = True
for i in range(100):
if done:
state = env.reset()
state = preprocess(state, [resize_height, width], final_height)
state = torch.cat((state, state, state, state))
action = random.randint(0,len(movement)-1)
next_state, reward, done, info = env.step(int(action))
# if reward>0:
# reward = 1
# else:
# reward = -1
reward /= 15
if reward == 0:
reward = -0.1
next_state = preprocess(next_state, [resize_height, width], final_height)
next_state = torch.cat((state[1:, :, :], next_state))
trans = transition(state, action, reward, next_state, done)
data.add(trans)
state = next_state
tau = 0
max_tau = 2000
decay_step = 0
farthest = 3000
cur_x = 1
#training loop
for episode in range(num_eps):
print('Episode {}'.format(episode+1))
state = env.reset()
state = preprocess(state, [resize_height, width], final_height)
state = torch.cat((state, state, state, state))
action = 0
episode_reward = 0
for step in range(max_steps):
tau += 1
#epsilon = stop_epsilon+(start_epsilon - stop_epsilon)*np.exp(-epsilon_decay*decay_step)
epsilon = start_epsilon * np.exp(1-(1/(cur_x/farthest)))
if epsilon < stop_epsilon:
epsilon = stop_epsilon
if random.random() < epsilon:
action = random.randint(0,len(movement)-1)
else:
q_val, action, q_vals = maxQ(state, model, device)
next_state, reward, done, info = env.step(int(action))
cur_x = info['x_pos']
if cur_x > farthest:
farthest = cur_x
# if reward > 0:
# reward = 1
# else:
# reward = -1
reward /= 15
if reward == 0:
reward = -0.1
episode_reward += reward
next_state = preprocess(next_state, [resize_height, width], final_height)
next_state = torch.cat((state[1:,:,:], next_state))
trans = transition(state, action, reward, next_state, done)
data.add(trans)
batch = data.get_batch(model, target_model, device, gamma)
loss, abs_err = train(model, device, optimizer, batch)
data.update_batch(batch['idx'], np.squeeze(torch.Tensor.numpy(abs_err)))
state = next_state
env.render()
#time.sleep(0.03)
if tau > max_tau:
target_model.load_state_dict(model.state_dict())
tau = 0
if done:
break
decay_step += step
with open(total_reward_file, 'a') as f:
f.write('{}\t{}\n'.format(episode_reward, step))
if episode % 5 == 0:
with open(model_file, 'wb') as f:
torch.save(model.state_dict(), f)
with open(data_file, 'wb') as f:
pickle.dump(data, f)
env.close()
def process_image(image, x, y, h, w):
image = image[y:y + h, x:x + w]
image = convert_to_gray_scale(image)
return image
# array = csv_train.get_values()
x_list = []
done = True
action = 0
for step in range(10):
if done:
state = env.reset()
state, reward, done, info = env.step(1)
env.render()
print(info['x_pos'])
x = info['x_pos'] - 22
y = env.unwrapped._y_position
print(env.unwrapped._y_position)
print(env.unwrapped._x_position)
h = 40 # 52
w = 60 # 20
print(env.observation_space.shape)
x_list.append(info['x_pos'])
image = env.render('rgb_array')
image = image[y:y + h, x:x + w]
# image = cv2.resize(image, dsize=(128, 120), interpolation=cv2.INTER_CUBIC)
print(image.shape)
image = convert_to_gray_scale(image)
print(image.shape)
# Build Bellman equation for the Q function
inputs[i:i + 1] = np.expand_dims(state, axis=0)
targets[i] = model.predict(state)
Q_sa = model.predict(state_new)
if done:
targets[i, action] = reward
else:
targets[i, action] = reward + gamma * np.max(Q_sa)
# Train network to output the Q function
model.train_on_batch(inputs, targets)
print('Learning Finished')
# THIRD STEP: Play!
observation = env.reset()
obs = np.expand_dims(observation, axis=0)
state = np.stack((obs, obs), axis=1)
done = False
tot_reward = 0.0
while not done:
env.render() # Uncomment to see game running
Q = model.predict(state)
action = np.argmax(Q)
observation, reward, done, info = env.step(action)
obs = np.expand_dims(observation, axis=0)
state = np.append(np.expand_dims(obs, axis=0), state[:, :1, :], axis=1)
tot_reward += reward
print('Game ended! Total reward: {}'.format(reward))
def replay_genome(genome, movements, gen):
env_expanded = gym_super_mario_bros.SuperMarioBrosEnv(frames_per_step=1,
rom_mode='vanilla')
env = BinarySpaceToDiscreteSpaceEnv(env_expanded, movements)
print('Number of genes: ', len(genome.connection_genes))
for gene in genome.connection_genes:
print(gene.in_node, gene.out_node, gene.weight, gene.innovation_number,
gene.type, gene.enabled)
done = True
unticked = 0
tick_interval = 1 / 30
last_tick_time = time.time()
fps = 0
frames = 0
last_fps_time = time.time()
for _ in range(500000):
unticked += time.time() - last_tick_time
last_tick_time = time.time()
ticked = False
# while unticked >= tick_interval:
if done:
state = env.reset()
state_downscaled = get_sensor_map(env_expanded)
action = genome.calculate_action(state_downscaled)
# print('\rFPS: {:.3f}'.format(fps), end=' ')
# print(vectofixedstr(action, 10), end=' ')
action = np.argmax(action)
print('\rtaking action', movements[action], end='', flush=True)
state, reward, done, info = env.step(action)
#filename = get_path_of('all_pictures/mario/')
#imsave(filename + 'mario_' + str(_) + '.png', state)
save_state = np.full((13, 10, 3), 255, dtype=np.int)
COLORS = [[250, 250, 250], [0, 0, 0], [196, 0, 0], [0, 0, 196]]
for i in range(13):
for j in range(10):
if state_downscaled[(i, j)] == -1:
save_state[(i, j)] = COLORS[3]
elif state_downscaled[(i, j)] == 0:
save_state[(i, j)] = COLORS[0]
else:
save_state[(i, j)] = COLORS[1]
save_state[(7, 2)] = COLORS[2]
# filename = get_path_of('all_pictures/input_downscaled/')
# imsave(filename + 'state_' + str(_) + '.png', save_state.astype(np.uint8))
# make_controller(movements[action], _, gen)
env.render()
if info["life"] <= 2:
died = True
break
ticked = True
frames += 1
unticked -= tick_interval
# if ticked:
# now = time.time()
# if now - last_fps_time >= 1:
# fps = frames / (now - last_fps_time)
# last_fps_time = now
# frames = 0
# else:
# time.sleep(0.001)
env.close()
class MarioEnv(Process):
def __init__(self,
env_id,
idx,
child_conn,
queue,
n_step,
is_render=False):
super(MarioEnv, self).__init__()
self.idx = idx
self.env_id = env_id
self.child_conn = child_conn
self.queue = queue
self.is_render = is_render
self.n_step = n_step
self.steps = 0
self.episodes = 0
self.accum_reward = 0
self.transition = None
self.prev_xpos = 0
self.prev_life = 0
def run(self):
super(MarioEnv, self).run()
self.env = gym_super_mario_bros.make(self.env_id)
self.env = BinarySpaceToDiscreteSpaceEnv(self.env, SIMPLE_MOVEMENT)
self.reset()
print('[ Worker %2d ] ' % (self.idx), end='')
print('Playing <', self.env_id, '>')
self.request_action(0, False)
while True:
action = self.child_conn.recv()
# print(SIMPLE_MOVEMENT[action])
next_state, reward, done, info = self.env.step(action)
force_done = False
if reward == -15:
force_done = True
self.steps += 1
self.accum_reward += reward
next_state = rgb2dataset(next_state)
if self.is_render and self.idx == 0:
self.env.render()
# make a transition
self.transition[:3, :, :] = self.transition[1:, :, :]
self.transition[3, :, :] = next_state
if done:
self.send_result(self.prev_xpos)
self.reset()
self.request_action(reward, force_done)
else:
self.request_action(reward, force_done)
self.prev_xpos = info['x_pos']
def reset(self):
state = self.env.reset()
state = rgb2dataset(state)
self.transition = np.zeros([4, 84, 84])
self.transition[-1, :] = state
self.steps = 0
self.episodes += 1
self.accum_reward = 0
def request_action(self, reward, done):
self.queue.put([self.idx, "OnStep", [self.transition, reward, done]])
def send_result(self, x_pos):
self.queue.put([
self.idx, "Result",
[self.episodes, self.steps, self.accum_reward, x_pos]
])
def main():
movement = SIMPLE_MOVEMENT
movement.append(['left', 'A'])
movement.append(['left', 'B'])
movement.append(['left', 'A', 'B'])
#movement.append(['B'])
#movement.append(['down'])
#movement.append(['up'])
env = gym_super_mario_bros.make('SuperMarioBros-1-1-v0')
env = BinarySpaceToDiscreteSpaceEnv(env, movement)
#channels is acting as the number of frames in history
#if resize_height and height are different, assert final_height < resize_height and image will be cropped
channels = 3
frames = 4
width = 128
resize_height = 180
final_height = 128
bottom_chop = 15
epsilon = 0.0
use_cuda = torch.cuda.is_available()
torch.manual_seed(1)
device = torch.device("cuda" if use_cuda else "cpu")
model = simple_net(channels, len(movement), device).to(device)
model_file = 'mario_agent'
model.load_state_dict(torch.load(model_file))
max_steps = 5000
num_eps = 1
for episode in range(num_eps):
print('Episode {}'.format(episode + 1))
state = env.reset()
state = preprocess(state, [resize_height, width, 3], final_height,
bottom_chop)
state = torch.cat((state, state, state, state))
action = 0
episode_reward = 0
for step in range(max_steps):
if step % 3 == 0:
if random.random() < epsilon:
action = random.randint(0, len(movement) - 1)
else:
q_val, action, q_vals = maxQ(state, model, device)
next_state, reward, done, info = env.step(int(action))
if reward > 0:
reward = 1
else:
reward = -1
episode_reward += reward
next_state = preprocess(next_state, [resize_height, width, 3],
final_height, bottom_chop)
next_state = torch.cat((state[3:, :, :], next_state))
state = next_state
env.render()
time.sleep(0.03)
if done:
break
env.close()
class Agent:
def __init__(self, level_name):
self.level_name = level_name
# setup environment
self.env = gym_super_mario_bros.make(level_name)
self.env = BinarySpaceToDiscreteSpaceEnv(self.env, SIMPLE_MOVEMENT)
# one hot encoded version of our actions
self.possible_actions = np.array(
np.identity(self.env.action_space.n, dtype=int).tolist())
# resest graph
tf.reset_default_graph()
# instantiate the DQNetwork
self.DQNetwork = DQNetwork(state_size, action_size, learning_rate)
# instantiate memory
self.memory = Memory(max_size=memory_size)
# initialize deque with zero images
self.stacked_frames = deque(
[np.zeros((100, 128), dtype=np.int) for i in range(stack_size)],
maxlen=4)
for i in range(pretrain_length):
# If it's the first step
if i == 0:
state = self.env.reset()
state, self.stacked_frames = stack_frames(
self.stacked_frames, state, True)
# Get next state, the rewards, done by taking a random action
choice = random.randint(1, len(self.possible_actions)) - 1
action = self.possible_actions[choice]
next_state, reward, done, _ = self.env.step(choice)
# stack the frames
next_state, self.stacked_frames = stack_frames(
self.stacked_frames, next_state, False)
# if the episode is finished (we're dead)
if done:
# we inished the episode
next_state = np.zeros(state.shape)
# add experience to memory
self.memory.add((state, action, reward, next_state, done))
# start a new episode
state = self.env.reset()
state, self.stacked_frames = stack_frames(
self.stacked_frames, state, True)
else:
# add experience to memory
self.memory.add((state, action, reward, next_state, done))
# our new state is now the next_state
state = next_state
# saver will help us save our model
self.saver = tf.train.Saver()
# setup tensorboard writer
self.writer = tf.summary.FileWriter("logs/")
# losses
tf.summary.scalar("Loss", self.DQNetwork.loss)
self.write_op = tf.summary.merge_all()
def predict_action(self, sess, explore_start, explore_stop, decay_rate,
decay_step, state, actions):
# first we randomize a number
exp_exp_tradeoff = np.random.rand()
explore_probability = explore_stop + (
explore_start - explore_stop) * np.exp(-decay_rate * decay_step)
if explore_probability > exp_exp_tradeoff:
# make a random action
choice = random.randint(1, len(self.possible_actions)) - 1
action = self.possible_actions[choice]
else:
# estimate the Qs values state
Qs = sess.run(self.DQNetwork.output,
feed_dict={
self.DQNetwork.inputs_:
state.reshape((1, *state.shape))
})
# take the biggest Q value (= best action)
choice = np.argmax(Qs)
action = self.possible_actions[choice]
return action, choice, explore_probability
def play_notebook(self):
import matplotlib.pyplot as plt
# imports to render env to gif
from JSAnimation.IPython_display import display_animation
from matplotlib import animation
from IPython.display import display
# http://mckinziebrandon.me/TensorflowNotebooks/2016/12/21/openai.html
def display_frames_as_gif(frames):
"""
Displays a list of frames as a gif, with controls
"""
#plt.figure(figsize=(frames[0].shape[1] / 72.0, frames[0].shape[0] / 72.0), dpi = 72)
patch = plt.imshow(frames[0])
plt.axis('off')
def animate(i):
patch.set_data(frames[i])
anim = animation.FuncAnimation(plt.gcf(),
animate,
frames=len(frames),
interval=50)
display(display_animation(anim, default_mode='loop'))
frames = []
with tf.Session() as sess:
total_test_rewards = []
# Load the model
self.saver.restore(sess, "models/{0}.cpkt".format(self.level_name))
for episode in range(1):
total_rewards = 0
state = self.env.reset()
state, self.stacked_frames = stack_frames(
self.stacked_frames, state, True)
print("****************************************************")
print("EPISODE ", episode)
while True:
# Reshape the state
state = state.reshape((1, *state_size))
# Get action from Q-network
# Estimate the Qs values state
Qs = sess.run(self.DQNetwork.output,
feed_dict={self.DQNetwork.inputs_: state})
# Take the biggest Q value (= the best action)
choice = np.argmax(Qs)
#Perform the action and get the next_state, reward, and done information
next_state, reward, done, _ = self.env.step(choice)
frames.append(self.env.render(mode='rgb_array'))
total_rewards += reward
if done:
print("Score", total_rewards)
total_test_rewards.append(total_rewards)
break
next_state, self.stacked_frames = stack_frames(
self.stacked_frames, next_state, False)
state = next_state
self.env.close()
display_frames_as_gif(frames)
def play(self):
with tf.Session() as sess:
total_test_rewards = []
# Load the model
self.saver.restore(sess, "models/{0}.cpkt".format(self.level_name))
for episode in range(1):
total_rewards = 0
state = self.env.reset()
state, self.stacked_frames = stack_frames(
self.stacked_frames, state, True)
print("****************************************************")
print("EPISODE ", episode)
while True:
# Reshape the state
state = state.reshape((1, *state_size))
# Get action from Q-network
# Estimate the Qs values state
Qs = sess.run(self.DQNetwork.output,
feed_dict={self.DQNetwork.inputs_: state})
# Take the biggest Q value (= the best action)
choice = np.argmax(Qs)
#Perform the action and get the next_state, reward, and done information
next_state, reward, done, _ = self.env.step(choice)
self.env.render()
total_rewards += reward
if done:
print("Score", total_rewards)
total_test_rewards.append(total_rewards)
break
next_state, self.stacked_frames = stack_frames(
self.stacked_frames, next_state, False)
state = next_state
self.env.close()
def train(self):
with tf.Session() as sess:
# initialize the variables
sess.run(tf.global_variables_initializer())
# initialize decay rate (that will be used to reduce epsilon)
decay_step = 0
for episode in range(total_episodes):
# set step to 0
step = 0
# initialize rewards of episode
episode_rewards = []
# make a new episode and opserve the first state
state = self.env.reset()
# remember that stack frame function
state, self.stacked_frames = stack_frames(
self.stacked_frames, state, True)
print("Episode:", episode)
while step < max_steps:
step += 1
#print("step:", step)
# increase decay_step
decay_step += 1
# predict an action
action, choice, explore_probability = self.predict_action(
sess, explore_start, explore_stop, decay_rate,
decay_step, state, self.possible_actions)
# perform the action and get the next_state, reward, and done information
next_state, reward, done, _ = self.env.step(choice)
if episode_render:
self.env.render()
# add the reward to total reward
episode_rewards.append(reward)
# the game is finished
if done:
print("done")
# the episode ends so no next state
next_state = np.zeros((110, 84), dtype=np.int)
next_state, self.stacked_frames = stack_frames(
self.stacked_frames, next_state, False)
# set step = max_steps to end episode
step = max_steps
# get total reward of the episode
total_reward = np.sum(episode_rewards)
print("Episode:", episode, "Total reward:",
total_reward, "Explore P:", explore_probability,
"Training Loss:", loss)
#rewards_list.append((episode, total_reward))
# store transition <s_i, a, r_{i+1}, s_{i+1}> in memory
self.memory.add(
(state, action, reward, next_state, done))
else:
# stack frame of the next state
next_state, self.stacked_frames = stack_frames(
self.stacked_frames, next_state, False)
# store transition <s_i, a, r_{i+1}, s_{i+1}> in memory
self.memory.add(
(state, action, reward, next_state, done))
# s_{i} := s_{i+1}
state = next_state
### Learning part
# obtain random mini-batch from memory
batch = self.memory.sample(batch_size)
states_mb = np.array([each[0] for each in batch], ndmin=3)
actions_mb = np.array([each[1] for each in batch])
rewards_mb = np.array([each[2] for each in batch])
next_states_mb = np.array([each[3] for each in batch],
ndmin=3)
dones_mb = np.array([each[4] for each in batch])
target_Qs_batch = []
# get Q values for next_state
Qs_next_state = sess.run(
self.DQNetwork.output,
feed_dict={self.DQNetwork.inputs_: next_states_mb})
# set Q_target = r if episode ends with s+1
for i in range(len(batch)):
terminal = dones_mb[i]
# if we are in a terminal state, only equals reward
if terminal:
target_Qs_batch.append(rewards_mb[i])
else:
target = rewards_mb[i] + gamma * np.max(
Qs_next_state[i])
target_Qs_batch.append(target)
targets_mb = np.array([each for each in target_Qs_batch])
loss, _ = sess.run(
[self.DQNetwork.loss, self.DQNetwork.optimizer],
feed_dict={
self.DQNetwork.inputs_: states_mb,
self.DQNetwork.target_Q: targets_mb,
self.DQNetwork.actions_: actions_mb
})
# write tf summaries
summary = sess.run(self.write_op,
feed_dict={
self.DQNetwork.inputs_: states_mb,
self.DQNetwork.target_Q: targets_mb,
self.DQNetwork.actions_: actions_mb
})
self.writer.add_summary(summary, episode)
self.writer.flush()
# save model every 5 episodes
if episode % 5 == 0:
self.saver.save(sess,
"models/{0}.cpkt".format(self.level_name))
print("Model Saved")
def train(num_episodes, episode_length, learning_rate , scenario = "deatmatch.cfg", map_path = 'map02', render= True):
#discount factor
discount_factor = 0.99
# 버퍼에 익스피리언스를 업데이트 하는 주기
learning_rate = 0.01
update_frequency = 5
store_frequency = 50
#아웃풋을 프린팅하는 주기
print_frequency = 1000
#total reward와 total loss를 저장할 변수를 초기화
total_reward = 0
total_loss = 0
old_q_value = 0
# episodic reward와 loss를 저장할 리스트를 초기화
rewards = []
losses = []
env = gym_super_mario_bros.make('SuperMarioBros-v0')
env = BinarySpaceToDiscreteSpaceEnv(env, COMPLEX_MOVEMENT)
env.reset()
actionDRQN = DRQN((240, 256, 3), 11,learning_rate)
targetDRQN = DRQN((240, 256, 3), 11,learning_rate)
#experience buffer cell_size
experiences = ExperienceReplay(1000000)
# 모델 저장
saver = tf.train.Saver({v.name: v for v in actionDRQN.parameters}, max_to_keep = 1)
#학습을 시작해보자
#샘플링을 위해 모든 변수를 초기화 시킨다. 그리고 버퍼에서 트렌지션을 storing한다.
sample = 10
store = 100
with tf.Session() as sess:
#모든 텐서플로우 변수를 초기화 한다.
sess.run(tf.global_variables_initializer())
for episode in range(num_episodes):
#새로운 에피소드를 시작한다.
env.reset()
for frame in range(episode_length):
env.render()
state = env.observation_space.shape
print(state)
action = actionDRQN.prediction.eval(feed_dict = {actionDRQN.input: state})
#env.step (action을 통하)
next_state, reward, done, info = env.step(action)
#reward를 업데이트
total_reward += reward
state= next_state
#game이 끝나면 break한다.
if done:
break
#transition을 버퍼에 넣는다.
if (frame%store)==0:
experience.appendToBuffer((s,action,reward))
#buffer에서 샘플을 뽑는다.
if (frame%sample) == 0:
memory = experiences.sample(1)
mem_frame=memory[0][0]
mem_reward = memory[0][2]
#train
Q1 = actionDRQN.output.eval(feed_dict = {actionDRQN.input : state})
Q2 = targetDRQN.output.eval(feed_dict = {targetDRQN.input : mem_frame})
#learning rate
learning_rate = actionDRQN.learning_rate.eval()
#Q value를 계산한다.
Qtarget = old_q_value + learning+_rate * (mem_reward + discount_factor * Q2 - old_q_value)
#update
old_q_value = Qtarget
# loss 계산
loss =actionDRQN.loss.eval(feed_dict = {actionDRQN.target_vector : Qtarget, actionDRQN.input : mem_frame})
p
#update loss
total_loss += loss
# 두 네트워크를 업데이트한다.
actionDRQN.update.run(feed_dict = {actionDRQN.target+vector : Qtarget, actionDRQN.input : mem_frame})
targetDRQN.update.run(feed_dict = {targetDRQN.target+vector : Qtarget, targetDRQN.input : mem_frame})
rewards.append((episode, total_reward))
losses.append((episode, total_loss))
total_reward = 0
total_loss = 0
def run(self):
global episode
env = gym_super_mario_bros.make('SuperMarioBros-1-1-v3')
env = BinarySpaceToDiscreteSpaceEnv(env, REALLY_COMPLEX_MOVEMENT)
step = 0
while episode < EPISODES:
done = False
max_x = 40
no_progress = 0
score = 0
state = env.reset()
# Making initial history with random actions
for _ in range(5):
next_state = state
state, _, _, _ = env.step(0)
state = preprocess(state)
history = np.stack((state, state, state, state), axis=2)
history = np.reshape([history], (1, 88, 128, 4))
while not done:
# Rendering code
# Seems to be causing error in Mac OS
if self.thread_num == 1:
env.render()
step += 1
self.t += 1
step_reward = 0
action, policy = self.get_action(history)
# Taking 3 steps with selected action
# Mimicking frame skip
for _ in range(6):
next_state, reward, done, info = env.step(action)
score += reward
step_reward += reward
if done:
break
# Kill Mario if Mario is making no progress for 10 seconds
x_now = info.get('x_pos')
# Handling exception x_pos = 65535
if x_now == 65535:
x_now = max_x
if max_x <= x_now:
max_x = x_now
no_progress = 0
else:
no_progress += 1
if no_progress == 150:
done = True
reward -= 1
step_reward -= 1
score -= 1
print("#", self.thread_num, " STUCK")
# Preprocessing each states
next_state = preprocess(next_state)
next_state = np.reshape([next_state], (1, 88, 128, 1))
next_history = np.append(next_state,
history[:, :, :, :3],
axis=3)
# Average policy max value
self.avg_p_max += np.amax(
self.actor.predict(np.float32(history / 255.)))
# Appending sample
self.append_sample(history, action, step_reward)
history = next_history
if self.t >= self.t_max or done:
#if done:
self.train_model(done)
self.update_local_model()
self.t = 0
if done:
# Recording training information
episode += 1
print("#", self.thread_num,
" episode:", episode, " score:",
format(score,
'.2f'), " step:", step, "max_x :", max_x)
stats = [score, self.avg_p_max / float(step), step]
for i in range(len(stats)):
self.sess.run(self.update_ops[i],
feed_dict={
self.summary_placeholders[i]:
float(stats[i])
})
summary_str = self.sess.run(self.summary_op)
self.summary_writer.add_summary(summary_str, episode + 1)
self.avg_p_max = 0
self.avg_loss = 0
step = 0
