def __init__(self, env_id, max_step = 1e5, prior_alpha = 0.6, prior_beta_start = 0.4,
epsilon_start = 1.0, epsilon_final = 0.01, epsilon_decay = 500,
batch_size = 32, gamma = 0.99, target_update_interval=1000, save_interval = 1e4,
):
self.prior_beta_start = prior_beta_start
self.max_step = int(max_step)
self.batch_size = batch_size
self.gamma = gamma
self.target_update_interval = target_update_interval
self.save_interval = save_interval
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.env = gym.make(env_id)
self.model = DuelingDQN(self.env).to(self.device)
self.target_model = DuelingDQN(self.env).to(self.device)
self.target_model.load_state_dict(self.model.state_dict())
self.replay_buffer = PrioritizedReplayBuffer(100000,alpha=prior_alpha)
self.optimizer = optim.Adam(self.model.parameters())
self.writer = SummaryWriter(comment="-{}-learner".format(self.env.unwrapped.spec.id))
# decay function
self.scheduler = optim.lr_scheduler.StepLR(self.optimizer,step_size=1000,gamma=0.99)
self.beta_by_frame = lambda frame_idx: min(1.0, self.prior_beta_start + frame_idx * (1.0 - self.prior_beta_start) / 1000)
self.epsilon_by_frame = lambda frame_idx: epsilon_final + (epsilon_start - epsilon_final) * math.exp(-1. * frame_idx / epsilon_decay)
def __init__(self, state_size, action_size, seed, network):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.network = network
# Q-Network
if self.network == "duel":
self.qnetwork_local = DuelingDQN(state_size, action_size,
seed).to(device)
self.qnetwork_target = DuelingDQN(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=LR)
else:
self.qnetwork_local = DQN(state_size, action_size, seed).to(device)
self.qnetwork_target = DQN(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def main():
args = argparser()
args.clip_rewards = False
env = make_atari(args.env)
env = wrap_atari_dqn(env, args)
seed = args.seed + 1122
utils.set_global_seeds(seed, use_torch=True)
env.seed(seed)
model = DuelingDQN(env)
model.load_state_dict(torch.load('model.pth', map_location='cpu'))
episode_reward, episode_length = 0, 0
state = env.reset()
while True:
if args.render:
env.render()
action, _ = model.act(torch.FloatTensor(np.array(state)), 0.)
next_state, reward, done, _ = env.step(action)
state = next_state
episode_reward += reward
episode_length += 1
if done:
state = env.reset()
print("Episode Length / Reward: {} / {}".format(
episode_length, episode_reward))
episode_reward = 0
episode_length = 0
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = DuelingDQN(state_size, action_size,
seed).to(device)
self.qnetwork_target = DuelingDQN(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
self.priority_alpha = 0.0 #current best: 03
self.priority_beta_start = 0.4
self.priority_beta_frames = BUFFER_SIZE
# Replay memory
self.memory = PrioritizedReplayMemory(BUFFER_SIZE, self.priority_alpha,
self.priority_beta_start,
self.priority_beta_frames)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def __init__(self, state_size, action_size, config=RLConfig()):
self.seed = random.seed(config.seed)
self.state_size = state_size
self.action_size = action_size
self.batch_size = config.batch_size
self.batch_indices = torch.arange(config.batch_size).long().to(device)
self.samples_before_learning = config.samples_before_learning
self.learn_interval = config.learning_interval
self.parameter_update_interval = config.parameter_update_interval
self.per_epsilon = config.per_epsilon
self.tau = config.tau
self.gamma = config.gamma
if config.useDuelingDQN:
self.qnetwork_local = DuelingDQN(state_size, action_size,
config.seed).to(device)
self.qnetwork_target = DuelingDQN(state_size, action_size,
config.seed).to(device)
else:
self.qnetwork_local = DQN(state_size, action_size,
config.seed).to(device)
self.qnetwork_target = DQN(state_size, action_size,
config.seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=config.learning_rate)
self.doubleDQN = config.useDoubleDQN
self.usePER = config.usePER
if self.usePER:
self.memory = PrioritizedReplayBuffer(config.buffer_size,
config.per_alpha)
else:
self.memory = ReplayBuffer(config.buffer_size)
self.t_step = 0
def __init__(self, state_size, action_size, seed, max_t=1000):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = DuelingDQN(state_size, action_size,
seed).to(device)
self.qnetwork_target = DuelingDQN(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
self.prio_b = PRIO_B
self.b_step = 0
self.max_b_step = 2000
self.learnFirst = True
def __init__(self,
env_id,
seed=0,
lr=1e-5,
n_step=3,
gamma=0.99,
n_workers=20,
max_norm=40,
target_update_interval=2500,
save_interval=5000,
batch_size=64,
buffer_size=1e6,
prior_alpha=0.6,
prior_beta=0.4,
publish_param_interval=32,
max_step=1e5):
self.env = gym.make(env_id)
self.seed = seed
self.lr = lr
self.n_step = n_step
self.gamma = gamma
self.max_norm = max_norm
self.target_update_interval = target_update_interval
self.save_interval = save_interval
self.publish_param_interval = publish_param_interval
self.batch_size = batch_size
self.prior_beta = prior_beta
self.max_step = max_step
self.buffer = CustomPrioritizedReplayBuffer(size=buffer_size,
alpha=prior_alpha)
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.model = DuelingDQN(self.env).to(self.device)
self.tgt_model = DuelingDQN(self.env).to(self.device)
self.tgt_model.load_state_dict(self.model.state_dict())
self.optimizer = torch.optim.RMSprop(self.model.parameters(),
self.lr,
alpha=0.95,
eps=1.5e-7,
centered=True)
self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer,
step_size=1000,
gamma=0.99)
self.beta_by_frame = lambda frame_idx: min(
1.0, self.prior_beta + frame_idx * (1.0 - self.prior_beta) / 1000)
self.batch_recorder = BatchRecorder(env_id=env_id,
env_seed=seed,
n_workers=n_workers,
buffer=self.buffer,
n_steps=n_step,
gamma=gamma,
max_episode_length=50000)
self.writer = SummaryWriter(
comment="-{}-learner".format(self.env.unwrapped.spec.id))
def __init__(self, state_size, action_size, configs, seed=0):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.double = configs["agent"]["double"]
self.dueling = configs["agent"]["dueling"]
self.lr = configs["lr"]
self.BUFFER_SIZE = int(float(configs["agent"]["buffer_size"]))
self.BATCH_SIZE = int(configs["batch_size"])
self.GAMMA = float(configs["gamma"])
self.TAU = float(configs["tau"])
self.UPDATE_EVERY = int(configs["update_every"])
# Q-Network
if not self.dueling:
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
else:
self.qnetwork_local = DuelingDQN(state_size, action_size,
seed).to(device)
self.qnetwork_target = DuelingDQN(state_size, action_size,
seed).to(device)
# LR mode
LR = float(self.lr["value"])
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
if self.lr["mode"] == "annealing":
LR = float(self.lr["max"])
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=LR)
self.scheduler = optim.lr_scheduler.ExponentialLR(self.optimizer,
gamma=self.GAMMA)
# Replay memory
self.memory = ReplayBuffer(action_size, self.BUFFER_SIZE,
self.BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def __init__(self, config):
"""Initialize an Agent object"""
self.seed = random.seed(config["general"]["seed"])
self.config = config
# Q-Network
self.q = DuelingDQN(config).to(DEVICE)
self.q_target = DuelingDQN(config).to(DEVICE)
self.optimizer = optim.RMSprop(self.q.parameters(),
lr=config["agent"]["learning_rate"])
self.criterion = F.mse_loss
self.memory = ReplayBuffer(config)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def __init__(self, state_size, action_size, seed):
"""
Initialize an Agent object.
:param state_size: dimension of each state;
:param action_size: dimension of each action;
:param seed: random seed.
"""
super().__init__(state_size, action_size, seed)
# Q-Network
self.network_local = DuelingDQN(state_size, action_size,
seed).to(DEVICE)
self.network_target = DuelingDQN(state_size, action_size,
seed).to(DEVICE)
self.optimizer = optim.Adam(self.network_local.parameters(), lr=LR)
def exploration(args, actor_id, param_queue):
writer = SummaryWriter(comment="-{}-eval".format(args.env))
args.clip_rewards = False
args.episode_life = False
env = make_atari(args.env)
env = wrap_atari_dqn(env, args)
seed = args.seed + actor_id
utils.set_global_seeds(seed, use_torch=True)
env.seed(seed)
model = DuelingDQN(env, args)
param = param_queue.get(block=True)
model.load_state_dict(param)
param = None
print("Received First Parameter!")
episode_reward, episode_length, episode_idx = 0, 0, 0
state = env.reset()
tb_dict = {k: [] for k in ['episode_reward', 'episode_length']}
while True:
action, _ = model.act(torch.FloatTensor(np.array(state)), 0.)
next_state, reward, done, _ = env.step(action)
state = next_state
episode_reward += reward
episode_length += 1
if done or episode_length == args.max_episode_length:
state = env.reset()
tb_dict["episode_reward"].append(episode_reward)
tb_dict["episode_length"].append(episode_length)
episode_reward = 0
episode_length = 0
episode_idx += 1
param = param_queue.get()
model.load_state_dict(param)
print(f"{datetime.now()} Updated Parameter..")
if (episode_idx *
args.num_envs_per_worker) % args.tb_interval == 0:
writer.add_scalar('evaluator/episode_reward_mean',
np.mean(tb_dict['episode_reward']),
episode_idx)
writer.add_scalar('evaluator/episode_reward_max',
np.max(tb_dict['episode_reward']),
episode_idx)
writer.add_scalar('evaluator/episode_reward_min',
np.min(tb_dict['episode_reward']),
episode_idx)
writer.add_scalar('evaluator/episode_reward_std',
np.std(tb_dict['episode_reward']),
episode_idx)
writer.add_scalar('evaluator/episode_length_mean',
np.mean(tb_dict['episode_length']),
episode_idx)
tb_dict['episode_reward'].clear()
tb_dict['episode_length'].clear()
def exploration(args, actor_id, n_actors, param_queue, send_queue,
req_param_queue):
writer = SummaryWriter(comment="-{}-actor{}".format(args.env, actor_id))
env = make_atari(args.env)
env = wrap_atari_dqn(env, args)
seed = args.seed + actor_id
utils.set_global_seeds(seed, use_torch=True)
env.seed(seed)
model = DuelingDQN(env)
epsilon = args.eps_base**(1 + actor_id / (n_actors - 1) * args.eps_alpha)
storage = BatchStorage(args.n_steps, args.gamma)
req_param_queue.put(True)
param = param_queue.get(block=True)
model.load_state_dict(param)
param = None
print("Received First Parameter!")
episode_reward, episode_length, episode_idx, actor_idx = 0, 0, 0, 0
state = env.reset()
while True:
action, q_values = model.act(torch.FloatTensor(np.array(state)),
epsilon)
next_state, reward, done, _ = env.step(action)
com_state = zlib.compress(np.array(state).tobytes())
storage.add(com_state, reward, action, done, q_values)
state = next_state
episode_reward += reward
episode_length += 1
actor_idx += 1
if done or episode_length == args.max_episode_length:
state = env.reset()
writer.add_scalar("actor/episode_reward", episode_reward,
episode_idx)
writer.add_scalar("actor/episode_length", episode_length,
episode_idx)
episode_reward = 0
episode_length = 0
episode_idx += 1
if actor_idx % args.update_interval == 0:
try:
req_param_queue.put(True)
param = param_queue.get(block=True)
model.load_state_dict(param)
print("Updated Parameter..")
except queue.Empty:
pass
if len(storage) == args.send_interval:
batch, prios = storage.make_batch()
send_queue.put((batch, prios))
batch, prios = None, None
storage.reset()
def __init__(self, worker_id, env_id, seed, epsilon, size, lock,
n_steps, gamma, send_interval, task_queue, buffer, max_episode_length):
mp.Process.__init__(self)
self.worker_id = worker_id
self.env = gym.make(env_id)
self.seed = seed
self.task_queue = task_queue
self.buffer = buffer
self.max_episode_length = max_episode_length
self.send_interval = send_interval
self.storage = BatchStorage(n_steps, gamma)
self.size = size
self.memory = []
self.model = DuelingDQN(self.env)
# self.writer = SummaryWriter(comment="-{}-actor{}".format(env_id, worker_id))
self.lock = lock
self.set_all_seeds()
self.epsilon = epsilon
def __init__(self, state_size, action_size, seed, model="QNetwork"):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
if model == "QNetwork":
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
if model == "QNetworkConvolutional":
self.qnetwork_local = QNetworkConvolutional(
state_size, action_size, seed).to(device)
self.qnetwork_target = QNetworkConvolutional(
state_size, action_size, seed).to(device)
if model == "DuelingDQN":
self.qnetwork_local = DuelingDQN(state_size, action_size,
seed).to(device)
self.qnetwork_target = DuelingDQN(state_size, action_size,
seed).to(device)
print("Model: " + model)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def __init__(self,
state_size,
action_size,
seed,
gamma=GAMMA,
buffer_size=BUFFER_SIZE,
batch_size=BATCH_SIZE,
update_every=UPDATE_EVERY,
lr=LR,
tau=TAU
):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.gamma = gamma
self.batch_size = batch_size
# Q-Network
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.model_local = DuelingDQN(state_size, action_size, seed).to(self.device)
self.model_target = DuelingDQN(state_size, action_size, seed).to(self.device)
self.optimizer = optim.Adam(self.model_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(
action_size=action_size,
buffer_size=BUFFER_SIZE,
batch_size=BATCH_SIZE,
seed=seed,
device=self.device
)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def __init__(self, state_size, action_size, seed):
"""
Initialize an Agent object.
:param state_size: dimension of each state;
:param action_size: dimension of each action;
:param seed: random seed.
"""
super().__init__(state_size, action_size, seed)
# Replay memory
self.memory = PrioritizedReplayBuffer(BUFFER_SIZE, BATCH_SIZE,
state_size, seed)
# Q-Network
self.network_local = DuelingDQN(state_size, action_size,
seed).to(DEVICE)
self.network_target = DuelingDQN(state_size, action_size,
seed).to(DEVICE)
self.optimizer = optim.Adam(self.network_local.parameters(), lr=LR)
def main():
learner_ip = get_environ()
args = argparser()
writer = SummaryWriter(comment="-{}-eval".format(args.env))
ctx = zmq.Context()
param_socket = ctx.socket(zmq.SUB)
param_socket.setsockopt(zmq.SUBSCRIBE, b'')
param_socket.setsockopt(zmq.CONFLATE, 1)
param_socket.connect('tcp://{}:52001'.format(learner_ip))
env = make_atari(args.env)
env = wrap_atari_dqn(env, args)
seed = args.seed + 1122
utils.set_global_seeds(seed, use_torch=True)
env.seed(seed)
model = DuelingDQN(env)
data = param_socket.recv(copy=False)
param = pickle.loads(data)
model.load_state_dict(param)
print("Loaded first parameter from learner")
episode_reward, episode_length, episode_idx = 0, 0, 0
state = env.reset()
while True:
if args.render:
env.render()
action, _ = model.act(torch.FloatTensor(np.array(state)), 0.01)
next_state, reward, done, _ = env.step(action)
state = next_state
episode_reward += reward
episode_length += 1
if done:
state = env.reset()
writer.add_scalar("eval/episode_reward", episode_reward,
episode_idx)
writer.add_scalar("eval/episode_length", episode_length,
episode_idx)
episode_reward = 0
episode_length = 0
episode_idx += 1
if episode_idx % args.eval_update_interval == 0:
data = param_socket.recv(copy=False)
param = pickle.loads(data)
model.load_state_dict(param)
def main():
args = argparser()
args.clip_rewards = False
args.episode_life=False
env = make_atari(args.env)
env = wrap_atari_dqn(env, args)
# seed = args.seed + 1122
# utils.set_global_seeds(seed, use_torch=True)
# env.seed(seed)
model = DuelingDQN(env, args)
model.load_state_dict(torch.load('model.pth', map_location='cpu'))
episode_reward, episode_length = 0, 0
state = env.reset()
if not os.path.exists('plays'):
os.mkdir('plays')
video = cv2.VideoWriter('plays/tmp.avi', cv2.VideoWriter_fourcc(*'DIVX'), 15, (160, 210))
while True:
img = env.render(mode='rgb_array')
model.zero_grad()
state = torch.tensor(state[np.newaxis, :], dtype=torch.float32, requires_grad=True)
value, action = model(state).max(1)
value = value[0]
action = action[0]
value.backward()
img_gradient = np.abs(state.grad.numpy())
img_gradient = np.sum(img_gradient, axis=(0,1))
img_gradient = (img_gradient - np.min(img_gradient)) / (np.max(img_gradient) - np.min(img_gradient))
img_gradient = img_gradient.transpose()
img_gradient = cv2.resize(img_gradient, (160, 210))[...,np.newaxis]
img_gradient = img_gradient * 255
masked_img = (img + img_gradient).astype(np.uint8)
masked_img = np.clip(masked_img, 0, 255)
video.write(masked_img)
next_state, reward, done, _ = env.step(int(action))
state = next_state
episode_reward += reward
episode_length += 1
if done:
state = env.reset()
print("Episode Length / Reward: {} / {}".format(episode_length, episode_reward))
video.release()
os.rename('plays/tmp.avi', f'plays/{args.env}-{episode_reward}.avi')
video = cv2.VideoWriter('plays/tmp.avi', cv2.VideoWriter_fourcc(*'DIVX'), 15, (160, 210))
episode_reward = 0
episode_length = 0
class DoubleDuelingDQNAgent(DoubleDQNAgent):
"""
Interacts with and learns from the environment.
Double Dueling DQN.
"""
def __init__(self, state_size, action_size, seed):
"""
Initialize an Agent object.
:param state_size: dimension of each state;
:param action_size: dimension of each action;
:param seed: random seed.
"""
super().__init__(state_size, action_size, seed)
# Q-Network
self.network_local = DuelingDQN(state_size, action_size,
seed).to(DEVICE)
self.network_target = DuelingDQN(state_size, action_size,
seed).to(DEVICE)
self.optimizer = optim.Adam(self.network_local.parameters(), lr=LR)
def exploration_eval(args, actor_id, param_queue):
writer = SummaryWriter(comment="-{}-eval".format(args.env))
args.clip_rewards = False
env = make_atari(args.env)
env = wrap_atari_dqn(env, args)
seed = args.seed + actor_id
utils.set_global_seeds(seed, use_torch=True)
env.seed(seed)
model = DuelingDQN(env)
param = param_queue.get(block=True)
model.load_state_dict(param)
param = None
print("Received First Parameter!")
episode_reward, episode_length, episode_idx = 0, 0, 0
state = env.reset()
while True:
action, _ = model.act(torch.FloatTensor(np.array(state)), 0.)
next_state, reward, done, _ = env.step(action)
state = next_state
episode_reward += reward
episode_length += 1
if done or episode_length == args.max_episode_length:
state = env.reset()
writer.add_scalar("evaluator/episode_reward", episode_reward,
episode_idx)
writer.add_scalar("evaluator/episode_length", episode_length,
episode_idx)
episode_reward = 0
episode_length = 0
episode_idx += 1
param = param_queue.get()
model.load_state_dict(param)
print("Updated Parameter..")
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed, model="QNetwork"):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
if model == "QNetwork":
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
if model == "QNetworkConvolutional":
self.qnetwork_local = QNetworkConvolutional(
state_size, action_size, seed).to(device)
self.qnetwork_target = QNetworkConvolutional(
state_size, action_size, seed).to(device)
if model == "DuelingDQN":
self.qnetwork_local = DuelingDQN(state_size, action_size,
seed).to(device)
self.qnetwork_target = DuelingDQN(state_size, action_size,
seed).to(device)
print("Model: " + model)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# Get max predicted Q values (for next states) from target model
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Get expected Q values from local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
print('episode: {}, Reward: {}'.format(episode, Reward))
break
def _eval():
for episode in range(10):
obs = env.reset()
Reward = 0
while True:
# env.render()
action = RL.choose_action(obs, True)
obs, reward, done, _ = env.step(action)
Reward += reward
if done:
print('Reward: {}'.format(Reward))
break
if __name__ == '__main__':
env = gym.make('CartPole-v0')
RL = DuelingDQN(env.observation_space.shape[0], env.action_space.n)
train()
_eval()
def actor(args, actor_id):
comm = global_dict['comm_local']
writer = SummaryWriter(log_dir=os.path.join(
args['log_dir'], f'{global_dict["unit_idx"]}-actor{actor_id}'))
num_envs = args['num_envs_per_worker']
envs = [
wrap_atari_dqn(make_atari(args['env']), args) for _ in range(num_envs)
]
if args['seed'] is not None:
seeds = args['seed'] + actor_id * num_envs + np.arange(num_envs)
utils.set_global_seeds(seeds[0], use_torch=True)
for seed, env in zip(seeds, envs):
env.seed(int(seed))
model = DuelingDQN(envs[0], args)
model = torch.jit.trace(model, torch.zeros((1, 4, 84, 84)))
_actor_id = (np.arange(num_envs) + actor_id *
num_envs) * args['num_units'] + global_dict['unit_idx']
n_actors = args['num_actors'] * num_envs * args['num_units']
epsilons = args['eps_base']**(1 + _actor_id /
(n_actors - 1) * args['eps_alpha'])
storages = [
BatchStorage(args['n_steps'], args['gamma']) for _ in range(num_envs)
]
recv_param_buf = bytearray(100 * 1024 * 1024)
recv_param_request = None
send_batch_request = None
actor_idx = 0
tb_idx = 0
episode_rewards = np.array([0] * num_envs)
episode_lengths = np.array([0] * num_envs)
states = np.array([env.reset() for env in envs])
tb_dict = {
key: []
for key in
['episode_reward', 'episode_length', 'kept_sample_percentage']
}
step_t = time.time()
inf_t = 0
sim_t = 0
def make_episilons():
return epsilons
while True:
if recv_param_request and recv_param_request.Test():
param = pickle.loads(recv_param_buf)
model.load_state_dict(param)
recv_param_request = None
if actor_idx * num_envs * n_actors <= args[
'initial_exploration_samples']: # initial random exploration
random_idx = np.arange(num_envs)
else:
random_idx, = np.where(
np.random.random(num_envs) <= make_episilons())
_t = time.time()
with torch.no_grad():
states_tensor = torch.tensor(states, dtype=torch.float32)
q_values = model(states_tensor).detach().numpy()
inf_t += time.time() - _t
actions = np.argmax(q_values, 1)
actions[random_idx] = np.random.choice(envs[0].action_space.n,
len(random_idx))
for i, (state, q_value, action, env, storage) in enumerate(
zip(states, q_values, actions, envs, storages)):
_t = time.time()
next_state, reward, done, _ = env.step(action)
try:
real_done = env.was_real_done
except:
real_done = done
sim_t += time.time() - _t
storage.add(np.array(state), reward, action, done, real_done,
q_value, _t, episode_lengths[i])
states[i] = next_state
episode_rewards[i] += reward
episode_lengths[i] += 1
if done or episode_lengths[i] == args['max_episode_length']:
states[i] = env.reset()
if real_done or episode_lengths[i] == args['max_episode_length']:
tb_idx += 1
tb_dict["episode_reward"].append(episode_rewards[i])
tb_dict["episode_length"].append(episode_lengths[i])
episode_rewards[i] = 0
episode_lengths[i] = 0
if tb_idx % args['tb_interval'] == 0:
writer.add_scalar('actor/1_episode_reward_mean',
np.mean(tb_dict['episode_reward']),
tb_idx)
writer.add_scalar('actor/2_episode_reward_max',
np.max(tb_dict['episode_reward']),
tb_idx)
writer.add_scalar('actor/3_episode_reward_min',
np.min(tb_dict['episode_reward']),
tb_idx)
writer.add_scalar('actor/4_episode_reward_std',
np.std(tb_dict['episode_reward']),
tb_idx)
writer.add_scalar('actor/5_episode_length_mean',
np.mean(tb_dict['episode_length']),
tb_idx)
tb_dict['episode_reward'].clear()
tb_dict['episode_length'].clear()
writer.add_scalar('actor/6_step_time',
(time.time() - step_t) /
np.sum(episode_lengths), tb_idx)
writer.add_scalar('actor/7_step_inference_time',
inf_t / np.sum(episode_lengths), tb_idx)
writer.add_scalar('actor/8_step_simulation_time',
sim_t / np.sum(episode_lengths), tb_idx)
writer.add_scalar(
'actor/9_kept_sample_percentage',
np.mean(tb_dict['kept_sample_percentage']), tb_idx)
inf_t = 0
sim_t = 0
step_t = time.time()
tb_dict['kept_sample_percentage'].clear()
actor_idx += 1
if actor_idx % args['update_interval'] == 0:
if recv_param_request is not None:
print(
f"actor {global_dict['unit_idx']}-{actor_id}: last recv param request is not complete!"
)
sys.stdout.flush()
else:
comm.Send(b'', dest=global_dict['rank_learner'])
recv_param_request = comm.Irecv(
buf=recv_param_buf, source=global_dict['rank_learner'])
if sum(len(storage)
for storage in storages) >= args['send_interval'] * num_envs:
batch = []
prios = []
for storage in storages:
_batch, _prios = storage.make_batch()
batch.append(_batch)
prios.append(_prios)
storage.reset()
batch = [np.concatenate(v) for v in zip(*batch)]
prios = np.concatenate(prios)
threshold = args['sample_filter_threshold']
prios_mask = prios > np.max(prios) * threshold
tb_dict['kept_sample_percentage'].append(
np.sum(prios_mask) / len(prios_mask))
prios = prios[prios_mask]
batch = [i[prios_mask] for i in batch]
data = pickle.dumps((batch, prios))
if send_batch_request is not None:
send_batch_request.wait()
send_batch_request = comm.Isend(data,
dest=global_dict['rank_replay'],
tag=utils.TAG_RECV_BATCH)
def learner(args):
comm_cross = global_dict['comm_cross']
hvd.init(comm=comm_cross)
torch.cuda.set_device(hvd.local_rank())
env = wrap_atari_dqn(make_atari(args['env']), args)
# utils.set_global_seeds(args['seed'], use_torch=True)
device = args['device']
model = DuelingDQN(env, args).to(device)
if os.path.exists('model.pth'):
# model.load_state_dict(torch.load('model.pth'))
pass
tgt_model = DuelingDQN(env, args).to(device)
del env
writer = SummaryWriter(log_dir=os.path.join(
args['log_dir'], f'{global_dict["unit_idx"]}-learner'))
# optimizer = torch.optim.SGD(model.parameters(), 1e-5 * args['num_units'], momentum=0.8)
# optimizer = torch.optim.RMSprop(model.parameters(), args['lr'], alpha=0.95, eps=1.5e-7, centered=True)
optimizer = torch.optim.Adam(model.parameters(),
args['lr'] * args['num_units'])
optimizer = hvd.DistributedOptimizer(
optimizer, named_parameters=model.named_parameters())
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
tgt_model.load_state_dict(model.state_dict())
if args['dynamic_gradient_clip']:
grad_norm_running_mean = args['gradient_norm_running_mean']
grad_norm_lambda = args['gradient_norm_lambda']
batch_queue = queue.Queue(maxsize=3)
prios_queue = queue.Queue(maxsize=4)
param_queue = queue.Queue(maxsize=3)
threading.Thread(target=recv_batch, args=(batch_queue, )).start()
threading.Thread(target=send_prios, args=(prios_queue, )).start()
threading.Thread(target=send_param, args=(param_queue, )).start()
if global_dict['unit_idx'] == 0:
threading.Thread(target=send_param_evaluator,
args=(param_queue, )).start()
prefetcher = data_prefetcher(batch_queue, args['cuda'])
learn_idx = 0
ts = time.time()
tb_dict = {
k: []
for k in [
'loss', 'grad_norm', 'max_q', 'mean_q', 'min_q',
'batch_queue_size', 'prios_queue_size'
]
}
first_rount = True
while True:
(*batch, idxes) = prefetcher.next()
if first_rount:
print("start training")
sys.stdout.flush()
first_rount = False
loss, prios, q_values = utils.compute_loss(model, tgt_model, batch,
args['n_steps'],
args['gamma'])
optimizer.zero_grad()
loss.backward()
if args['dynamic_gradient_clip']:
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(),
grad_norm_running_mean * args['clipping_threshold'])
grad_norm_running_mean = grad_norm_running_mean * grad_norm_lambda + \
min(grad_norm, grad_norm_running_mean * args['clipping_threshold']) * (1-grad_norm_lambda)
else:
grad_norm = torch.norm(
torch.stack([
torch.norm(p.grad.detach(), 2) for p in model.parameters()
]), 2)
# global_prios_sum = np.array(prios_sum)
# comm_cross.Allreduce(MPI.IN_PLACE, global_prios_sum.data)
# global_prios_sum = float(global_prios_sum)
# scale = prios_sum / global_prios_sum
if args['dynamic_gradient_clip'] and args[
'dropping_threshold'] and grad_norm > grad_norm_running_mean * args[
'dropping_threshold']:
pass
else:
optimizer.step()
prios_queue.put((idxes, prios))
learn_idx += 1
tb_dict["loss"].append(float(loss))
tb_dict["grad_norm"].append(float(grad_norm))
tb_dict["max_q"].append(float(torch.max(q_values)))
tb_dict["mean_q"].append(float(torch.mean(q_values)))
tb_dict["min_q"].append(float(torch.min(q_values)))
tb_dict["batch_queue_size"].append(batch_queue.qsize())
tb_dict["prios_queue_size"].append(prios_queue.qsize())
if learn_idx % args['target_update_interval'] == 0:
tgt_model.load_state_dict(model.state_dict())
if learn_idx % args['save_interval'] == 0 and global_dict[
'unit_idx'] == 0:
torch.save(model.state_dict(), "model.pth")
if learn_idx % args['publish_param_interval'] == 0:
param_queue.put(model.state_dict())
if learn_idx % args['tb_interval'] == 0:
bps = args['tb_interval'] / (time.time() - ts)
for i, (k, v) in enumerate(tb_dict.items()):
writer.add_scalar(f'learner/{i+1}_{k}', np.mean(v), learn_idx)
v.clear()
writer.add_scalar(f"learner/{i+2}_BPS", bps, learn_idx)
ts = time.time()
class PERDoubleDuelingDQNAgent(DoubleDuelingDQNAgent):
"""
Interacts with and learns from the environment.
Double Dueling DQN with prioritized experience replay.
"""
def __init__(self, state_size, action_size, seed):
"""
Initialize an Agent object.
:param state_size: dimension of each state;
:param action_size: dimension of each action;
:param seed: random seed.
"""
super().__init__(state_size, action_size, seed)
# Replay memory
self.memory = PrioritizedReplayBuffer(BUFFER_SIZE, BATCH_SIZE,
state_size, seed)
# Q-Network
self.network_local = DuelingDQN(state_size, action_size,
seed).to(DEVICE)
self.network_target = DuelingDQN(state_size, action_size,
seed).to(DEVICE)
self.optimizer = optim.Adam(self.network_local.parameters(), lr=LR)
def learn(self, experiences, gamma):
"""
Update value parameters using given batch of experience tuples.
:param experiences: (Tuple[torch.Tensor]) tuple of (s, a, r, s', done) tuples;
:param gamma: discount factor.
"""
tree_idx, states, actions, rewards, next_states, dones, ISWeights = experiences
# Get expected Q values from local model
Q_expected = self.network_local(states).gather(1, actions)
# Get next actions based on local network
next_actions = self.network_local(next_states).detach().max(
1)[1].unsqueeze(1)
# Get max predicted Q values (for next states) from target model based on local model next actions
Q_targets_next = self.network_target(next_states).detach().gather(
1, next_actions)
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Update transition priorities
self.memory.batch_update(tree_idx, np.ravel(np.abs(Q_targets.numpy())))
# Compute loss
loss = (torch.Tensor(ISWeights).float().to(DEVICE) *
F.mse_loss(Q_expected, Q_targets)).mean()
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.network_local, self.network_target, TAU)
def vector_exploration(args, actor_id, n_actors, replay_ip, param_queue):
ctx = zmq.Context()
batch_socket = ctx.socket(zmq.DEALER)
batch_socket.setsockopt(zmq.IDENTITY,
pickle.dumps('actor-{}'.format(actor_id)))
batch_socket.connect('tcp://{}:51001'.format(replay_ip))
outstanding = 0
writer = SummaryWriter(comment="-{}-actor{}".format(args.env, actor_id))
num_envs = args.num_envs_per_worker
envs = [
wrap_atari_dqn(make_atari(args.env), args) for _ in range(num_envs)
]
if args.seed is not None:
seeds = args.seed + actor_id * num_envs + np.arange(num_envs)
utils.set_global_seeds(seeds[0], use_torch=True)
for seed, env in zip(seeds, envs):
env.seed(int(seed))
model = DuelingDQN(envs[0], args)
model = torch.jit.trace(model, torch.zeros((1, 4, 84, 84)))
_actor_id = np.arange(num_envs) + actor_id * num_envs
n_actors = n_actors * num_envs
epsilons = args.eps_base**(1 + _actor_id / (n_actors - 1) * args.eps_alpha)
storages = [
BatchStorage(args.n_steps, args.gamma) for _ in range(num_envs)
]
param = param_queue.get(block=True)
model.load_state_dict(param)
param = None
print("%d: Received First Parameter!" % actor_id)
actor_idx = 0
tb_idx = 0
episode_rewards = np.array([0] * num_envs)
episode_lengths = np.array([0] * num_envs)
states = np.array([env.reset() for env in envs])
tb_dict = {key: [] for key in ['episode_reward', 'episode_length']}
step_t = time.time()
ref_t = 0
sim_t = 0
while True:
if actor_idx * num_envs * n_actors <= args.initial_exploration_samples: # initial random exploration
random_idx = np.arange(num_envs)
else:
random_idx, = np.where(np.random.random(num_envs) <= epsilons)
_t = time.time()
with torch.no_grad():
states_tensor = torch.tensor(states, dtype=torch.float32)
q_values = model(states_tensor).detach().numpy()
ref_t += time.time() - _t
actions = np.argmax(q_values, 1)
actions[random_idx] = np.random.choice(envs[0].action_space.n,
len(random_idx))
for i, (state, q_value, action, env, storage) in enumerate(
zip(states, q_values, actions, envs, storages)):
_t = time.time()
next_state, reward, done, _ = env.step(action)
sim_t += time.time() - _t
storage.add(np.array(state), reward, action, done, q_value, _t,
episode_lengths[i])
states[i] = next_state
episode_rewards[i] += reward
episode_lengths[i] += 1
if done or episode_lengths[i] == args.max_episode_length:
states[i] = env.reset()
tb_idx += 1
tb_dict["episode_reward"].append(episode_rewards[i])
tb_dict["episode_length"].append(episode_lengths[i])
episode_rewards[i] = 0
episode_lengths[i] = 0
if tb_idx % args.tb_interval == 0:
writer.add_scalar('actor/episode_reward_mean',
np.mean(tb_dict['episode_reward']),
tb_idx)
writer.add_scalar('actor/episode_reward_max',
np.max(tb_dict['episode_reward']),
tb_idx)
writer.add_scalar('actor/episode_reward_min',
np.min(tb_dict['episode_reward']),
tb_idx)
writer.add_scalar('actor/episode_reward_std',
np.std(tb_dict['episode_reward']),
tb_idx)
writer.add_scalar('actor/episode_length_mean',
np.mean(tb_dict['episode_length']),
tb_idx)
tb_dict['episode_reward'].clear()
tb_dict['episode_length'].clear()
writer.add_scalar('actor/step_time',
(time.time() - step_t) /
np.sum(episode_lengths), tb_idx)
writer.add_scalar('actor/step_inference_time',
ref_t / np.sum(episode_lengths), tb_idx)
writer.add_scalar('actor/step_simulation_time',
sim_t / np.sum(episode_lengths), tb_idx)
ref_t = 0
sim_t = 0
step_t = time.time()
actor_idx += 1
if actor_idx % args.update_interval == 0:
try:
param = param_queue.get(block=False)
model.load_state_dict(param)
print("%d: Updated Parameter.." % actor_id)
except queue.Empty:
pass
if sum(len(storage)
for storage in storages) >= args.send_interval * num_envs:
batch = []
prios = []
for storage in storages:
_batch, _prios = storage.make_batch()
batch.append(_batch)
prios.append(_prios)
storage.reset()
batch = [np.concatenate(v) for v in zip(*batch)]
prios = np.concatenate(prios)
data = pickle.dumps((batch, prios))
batch, prios = None, None
while outstanding >= args.max_outstanding:
batch_socket.recv()
outstanding -= 1
batch_socket.send(data, copy=False)
outstanding += 1
class train_DQN():
def __init__(self, env_id, max_step = 1e5, prior_alpha = 0.6, prior_beta_start = 0.4,
epsilon_start = 1.0, epsilon_final = 0.01, epsilon_decay = 500,
batch_size = 32, gamma = 0.99, target_update_interval=1000, save_interval = 1e4,
):
self.prior_beta_start = prior_beta_start
self.max_step = int(max_step)
self.batch_size = batch_size
self.gamma = gamma
self.target_update_interval = target_update_interval
self.save_interval = save_interval
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.env = gym.make(env_id)
self.model = DuelingDQN(self.env).to(self.device)
self.target_model = DuelingDQN(self.env).to(self.device)
self.target_model.load_state_dict(self.model.state_dict())
self.replay_buffer = PrioritizedReplayBuffer(100000,alpha=prior_alpha)
self.optimizer = optim.Adam(self.model.parameters())
self.writer = SummaryWriter(comment="-{}-learner".format(self.env.unwrapped.spec.id))
# decay function
self.scheduler = optim.lr_scheduler.StepLR(self.optimizer,step_size=1000,gamma=0.99)
self.beta_by_frame = lambda frame_idx: min(1.0, self.prior_beta_start + frame_idx * (1.0 - self.prior_beta_start) / 1000)
self.epsilon_by_frame = lambda frame_idx: epsilon_final + (epsilon_start - epsilon_final) * math.exp(-1. * frame_idx / epsilon_decay)
def update_target(self,current_model, target_model):
target_model.load_state_dict(current_model.state_dict())
def compute_td_loss(self,batch_size, beta):
state, action, reward, next_state, done, weights, indices = self.replay_buffer.sample(batch_size, beta)
state = torch.FloatTensor(state).to(self.device)
next_state = torch.FloatTensor(next_state).to(self.device)
action = torch.LongTensor(action).to(self.device)
reward = torch.FloatTensor(reward).to(self.device)
done = torch.FloatTensor(done).to(self.device)
weights = torch.FloatTensor(weights).to(self.device)
batch = (state, action, reward, next_state, done, weights)
# q_values = self.model(state)
# next_q_values = self.target_model(next_state)
# q_value = q_values.gather(1, action.unsqueeze(1)).squeeze(1)
# next_q_value = next_q_values.max(1)[0]
# expected_q_value = reward + self.gamma * next_q_value * (1 - done)
# td_error = torch.abs(expected_q_value.detach() - q_value)
# loss = (td_error).pow(2) * weights
# prios = loss+1e-5#0.9 * torch.max(td_error)+(1-0.9)*td_error
# loss = loss.mean()
loss, prios = utils.compute_loss(self.model,self.target_model, batch,1)
self.optimizer.zero_grad()
loss.backward()
self.scheduler.step()
self.replay_buffer.update_priorities(indices, prios)
self.optimizer.step()
return loss
def train(self):
losses = []
all_rewards = []
episode_reward = 0
episode_idx = 0
episode_length = 0
state = self.env.reset()
for frame_idx in range(self.max_step):
epsilon = self.epsilon_by_frame(frame_idx)
action,_ = self.model.act(torch.FloatTensor((state)).to(self.device), epsilon)
next_state, reward, done, _ = self.env.step(action)
self.replay_buffer.add(state, action, reward, next_state, done)
state = next_state
episode_reward += reward
episode_length += 1
if done:
state = self.env.reset()
all_rewards.append(episode_reward)
self.writer.add_scalar("actor/episode_reward", episode_reward, episode_idx)
self.writer.add_scalar("actor/episode_length", episode_length, episode_idx)
# print("episode: ",episode_idx, " reward: ", episode_reward)
episode_reward = 0
episode_length = 0
episode_idx += 1
if len(self.replay_buffer) > self.batch_size:
beta = self.beta_by_frame(frame_idx)
loss = self.compute_td_loss(self.batch_size, beta)
losses.append(loss.item())
self.writer.add_scalar("learner/loss", loss, frame_idx)
if frame_idx % self.target_update_interval == 0:
print("update target...")
self.update_target(self.model, self.target_model)
if frame_idx % self.save_interval == 0 or frame_idx == self.max_step-1:
print("save model...")
self.save_model(frame_idx)
def save_model(self, idx):
torch.save(self.model.state_dict(), "./model{}.pth".format(idx))
def load_model(self,idx):
with open("model{}.pth".format(idx), "rb") as f:
print("loading weights_{}".format(idx))
self.model.load_state_dict(torch.load(f,map_location="cpu"))
def train(args, n_actors, batch_queue, prios_queue, param_queue):
env = wrapper.make_atari(args.env)
env = wrapper.wrap_atari_dqn(env, args)
utils.set_global_seeds(args.seed, use_torch=True)
model = DuelingDQN(env).to(args.device)
tgt_model = DuelingDQN(env).to(args.device)
tgt_model.load_state_dict(model.state_dict())
writer = SummaryWriter(comment="-{}-learner".format(args.env))
# optimizer = torch.optim.Adam(model.parameters(), args.lr)
optimizer = torch.optim.RMSprop(model.parameters(),
args.lr,
alpha=0.95,
eps=1.5e-7,
centered=True)
check_connection(n_actors)
param_queue.put(model.state_dict())
learn_idx = 0
ts = time.time()
while True:
*batch, idxes = batch_queue.get()
loss, prios = utils.compute_loss(model, tgt_model, batch, args.n_steps,
args.gamma)
grad_norm = utils.update_parameters(loss, model, optimizer,
args.max_norm)
prios_queue.put((idxes, prios))
batch, idxes, prios = None, None, None
learn_idx += 1
writer.add_scalar("learner/loss", loss, learn_idx)
writer.add_scalar("learner/grad_norm", grad_norm, learn_idx)
if learn_idx % args.target_update_interval == 0:
print("Updating Target Network..")
tgt_model.load_state_dict(model.state_dict())
if learn_idx % args.save_interval == 0:
print("Saving Model..")
torch.save(model.state_dict(), "model.pth")
if learn_idx % args.publish_param_interval == 0:
param_queue.put(model.state_dict())
if learn_idx % args.bps_interval == 0:
bps = args.bps_interval / (time.time() - ts)
print("Step: {:8} / BPS: {:.2f}".format(learn_idx, bps))
writer.add_scalar("learner/BPS", bps, learn_idx)
ts = time.time()
def train(args, n_actors, batch_queue, prios_queue, param_queue):
env = wrapper.make_atari(args.env)
env = wrapper.wrap_atari_dqn(env, args)
utils.set_global_seeds(args.seed, use_torch=True)
model = DuelingDQN(env, args).to(args.device)
# model.load_state_dict(torch.load('model_30h.pth'))
tgt_model = DuelingDQN(env, args).to(args.device)
tgt_model.load_state_dict(model.state_dict())
writer = SummaryWriter(comment="-{}-learner".format(args.env))
optimizer = torch.optim.Adam(model.parameters(), args.lr)
# optimizer = torch.optim.RMSprop(model.parameters(), args.lr, alpha=0.95, eps=1.5e-7, centered=True)
check_connection(n_actors)
param_queue.put(model.state_dict())
learn_idx = 0
ts = time.time()
tb_dict = {
k: []
for k in ['loss', 'grad_norm', 'max_q', 'mean_q', 'min_q']
}
while True:
*batch, idxes = batch_queue.get()
loss, prios, q_values = utils.compute_loss(model, tgt_model, batch,
args.n_steps, args.gamma)
grad_norm = utils.update_parameters(loss, model, optimizer,
args.max_norm)
prios_queue.put((idxes, prios))
batch, idxes, prios = None, None, None
learn_idx += 1
tb_dict["loss"].append(float(loss))
tb_dict["grad_norm"].append(float(grad_norm))
tb_dict["max_q"].append(float(torch.max(q_values)))
tb_dict["mean_q"].append(float(torch.mean(q_values)))
tb_dict["min_q"].append(float(torch.min(q_values)))
if args.soft_target_update:
tau = args.tau
for p_tgt, p in zip(tgt_model.parameters(), model.parameters()):
p_tgt.data *= 1 - tau
p_tgt.data += tau * p
elif learn_idx % args.target_update_interval == 0:
print("Updating Target Network..")
tgt_model.load_state_dict(model.state_dict())
if learn_idx % args.save_interval == 0:
print("Saving Model..")
torch.save(model.state_dict(), "model.pth")
if learn_idx % args.publish_param_interval == 0:
param_queue.put(model.state_dict())
if learn_idx % args.tb_interval == 0:
bps = args.tb_interval / (time.time() - ts)
print("Step: {:8} / BPS: {:.2f}".format(learn_idx, bps))
writer.add_scalar("learner/BPS", bps, learn_idx)
for k, v in tb_dict.items():
writer.add_scalar(f'learner/{k}', np.mean(v), learn_idx)
v.clear()
ts = time.time()
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed, max_t=1000):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = DuelingDQN(state_size, action_size,
seed).to(device)
self.qnetwork_target = DuelingDQN(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
self.prio_b = PRIO_B
self.b_step = 0
self.max_b_step = 2000
self.learnFirst = True
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
#self.memory.add(state, action, reward, next_state, done)
# Hassan : Save the experience in prioritized replay memory
self.memory.prio_add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
#experiences = self.memory.sample()
#self.learn(experiences, GAMMA)
# Hassan : prioritized replay memory
self.b_step = self.b_step + 1
experiences, indices = self.memory.prio_sample()
self.learn(experiences, GAMMA, indices)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def get_beta(self, t):
'''
Return the current exponent β based on its schedul. Linearly anneal β
from its initial value β0 to 1, at the end of learning.
:param t: integer. Current time step in the episode
:return current_beta: float. Current exponent beta
'''
#f_frac = min(float(t) / self.max_b_step, 1.0)
#current_beta = self.prio_b + f_frac * (1. - self.prio_b)
#current_beta = min(1,current_beta)
self.prio_b = min(1, self.prio_b + PRIO_B_INC)
return self.prio_b
def learn(self, experiences, gamma, indices):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones, probabilities = experiences
## TODO: compute and minimize the loss
"*** YOUR CODE HERE ***"
# Get max predicted Q values (for next states) from target model
# Hassan : Action is selected using greedy policy
#Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
# Hassan : Double DQN
# Selecting actions which maximizes while taking w (qnetwork_local)
next_actions = self.qnetwork_local(next_states).detach().argmax(
dim=1).unsqueeze(1)
#next_actions_test = self.qnetwork_local(next_states).detach().max(1)[1].unsqueeze(1) # Hassan : from the example
#print(torch.sum(next_actions-next_actions_test)) # Hassan : no difference found
# Selecting q values of these actions using w' (qnetwork_target)
Q_targets_next = self.qnetwork_target(next_states).gather(
1, next_actions)
# Compute Q targets for current states
# Hassan : This is TD Target
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Get expected Q values from local model
# Hassan : This is current value
Q_expected = self.qnetwork_local(states).gather(1, actions)
#Hassan : Compute the td_error
td_error = Q_targets - Q_expected
#print(td_error.detach().numpy())
#self.prio_b = min(1, PRIO_B_INC+self.prio_b)
f_currbeta = self.get_beta(0)
#print(f_currbeta)
#f_currbeta = self.get_beta(self.b_step)
#print(self.b_step)
#print(t)
#print(self.prio_b)
weights_importance = probabilities.mul_(
self.memory.__len__()).pow_(-f_currbeta)
# Hassan : calculate max_weights_importance
#probabilities_min = min(self.memory.priorities)/self.memory.cum_priorities
probabilities_min = self.memory.min_priority / self.memory.cum_priorities
max_weights_importance = (probabilities_min *
self.memory.__len__())**(-f_currbeta)
# Hassan : divide the weights importance with the max_weights_importance
# Hassan : Improvement why not calculating the max_weights_importance = max(weights_importance)??
# Hassan : this will only calculating on the current list not the complete one
#print(weights_importance)
#print(weights_importance.max(0)[0])
#print(max_weights_importance)
#if self.learnFirst:
# self.learnFirst = False
#else :
# max_weights_importance = max_weights_importance[0]
weights_final = weights_importance.div_(max_weights_importance)
square_weighted_error = td_error.pow_(2).mul_(weights_final)
loss = square_weighted_error.mean()
# Hassan : after the observations observation from example, update was done after the weights calculation
if self.prio_b > 0.5:
self.memory.prio_update(indices,
td_error.detach().numpy(), PRIO_E, PRIO_A)
# Compute loss
#loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
# Hassan : Here not after C steps w is changed though cahnged slightly after every learn step
# Hassan : We can modify to change this after ever C steps
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
