def test_per_nstep(self):
"""
PrioritizedReplayBuffer.on_episode_end() ignores Exception
Ref: https://gitlab.com/ymd_h/cpprb/-/issues/111
"""
rb = PrioritizedReplayBuffer(32, {
"rew": {},
"done": {}
},
Nstep={
"size": 4,
"rew": "rew",
"gamma": 0.5
})
for _ in range(10):
rb.add(rew=0.5, done=0.0)
rb.add(rew=0.5, done=1.0)
rb.on_episode_end()
s = rb.sample(16)
self.assertIn("discounts", s)
def test_sample(self):
buffer_size = 500
obs_shape = (84, 84, 3)
act_dim = 4
rb = PrioritizedReplayBuffer(buffer_size, {
"obs": {
"shape": obs_shape
},
"act": {
"shape": act_dim
},
"rew": {},
"done": {}
},
next_of="obs")
obs = np.zeros(obs_shape)
act = np.ones(act_dim)
rew = 1
done = 0
rb.add(obs=obs, act=act, rew=rew, next_obs=obs, done=done)
ps = 1.5
rb.add(obs=obs,
act=act,
rew=rew,
next_obs=obs,
done=done,
priorities=ps)
self.assertAlmostEqual(rb.get_max_priority(), 1.5)
obs = np.stack((obs, obs))
act = np.stack((act, act))
rew = (1, 0)
done = (0.0, 1.0)
rb.add(obs=obs, act=act, rew=rew, next_obs=obs, done=done)
ps = (0.2, 0.4)
rb.add(obs=obs,
act=act,
rew=rew,
next_obs=obs,
done=done,
priorities=ps)
sample = rb.sample(64)
w = sample["weights"]
i = sample["indexes"]
rb.update_priorities(i, w * w)
def test_PrioritizedReplayBuffer_with_single_step_with_priorities(self):
buffer_size = 256
obs_shape = (3, 4)
batch_size = 10
rb = PrioritizedReplayBuffer(buffer_size,
{"obs": {
"shape": obs_shape
}})
v = {"obs": np.ones(shape=obs_shape), "priorities": 0.5}
rb.add(**v)
rb.sample(batch_size)
for _ in range(100):
rb.add(**v)
rb.sample(batch_size)
def test_PrioritizedReplayBuffer_with_multiple_steps(self):
buffer_size = 256
obs_shape = (3, 4)
step_size = 32
batch_size = 10
rb = PrioritizedReplayBuffer(buffer_size,
{"obs": {
"shape": obs_shape
}})
v = {"obs": np.ones(shape=(step_size, *obs_shape))}
rb.add(**v)
rb.sample(batch_size)
for _ in range(100):
rb.add(**v)
rb.sample(batch_size)
def test_read_only_priority(self):
buffer_size = 100
batch_size = 32
env_dict = {"done": {}}
done = np.zeros(2)
ps = np.ones_like(done)
ps.setflags(write=False)
rb = PrioritizedReplayBuffer(buffer_size, env_dict)
rb.add(done=done, priority=ps)
sample = rb.sample(batch_size)
ps2 = sample["weights"]
ps2.setflags(write=False)
rb.update_priorities(sample["indexes"], ps2)
# Directly access internal PrioritizedBuffer,
# since ChainerRL/PrioritizedReplayBuffer has no API to set priority.
cprb.memory.append([{
"state": o,
"action": a,
"reward": r,
"next_state": o,
"is_state_terminal": d
}],
priority=p)
perfplot.plot(
time_unit="ms",
setup=lambda n: n,
kernels=[
lambda n: bprb.sample(n, beta=beta),
lambda n: rprb.sample(n, beta=beta),
sample_c(cprb), lambda n: prb.sample(n, beta=beta)
],
labels=["OpenAI/Baselines", "Ray/RLlib", "Chainer/ChainerRL", "cpprb"],
n_range=[2**n for n in range(1, 9)],
xlabel="Batch size",
logx=False,
logy=False,
equality_check=None)
plt.title("Prioritized Replay Buffer Sample Speed")
plt.savefig("PrioritizedReplayBuffer_sample.png",
transparent=True,
bbox_inches="tight")
plt.close()
class RainbowAgent:
"""Agent interacting with environment.
Attribute:
env (gym.Env): openAI Gym environment
memory (PrioritizedReplayBuffer): replay memory to store transitions
batch_size (int): batch size for sampling
target_update (int): period for target model's hard update
gamma (float): discount factor
dqn (Network): model to train and select actions
dqn_target (Network): target model to update
optimizer (torch.optim): optimizer for training dqn
transition (list): transition information including
state, action, reward, next_state, done
v_min (float): min value of support
v_max (float): max value of support
atom_size (int): the unit number of support
support (torch.Tensor): support for categorical dqn
use_n_step (bool): whether to use n_step memory
n_step (int): step number to calculate n-step td error
memory_n (ReplayBuffer): n-step replay buffer
"""
def __init__(
self,
env: gym.Env,
memory_size: int,
batch_size: int,
target_update: int,
gamma: float = 0.99,
# PER parameters
alpha: float = 0.2,
beta: float = 0.6,
prior_eps: float = 1e-6,
# Categorical DQN parameters
v_min: float = 0.0,
v_max: float = 200.0,
atom_size: int = 51,
# N-step Learning
n_step: int = 3,
# Convergence parameters
convergence_window: int = 100,
convergence_window_epsilon_p: int = 10,
convergence_avg_score: float = 195.0,
convergence_avg_epsilon: float = 0.0524, # 3 degs converted to rads
convergence_avg_epsilon_p: float = 0.0174, # 1 deg/s converted to rad/s
# Tensorboard parameters
model_name: str = "snake_joint",
):
"""Initialization.
Args:
env (gym.Env): openAI Gym environment
memory_size (int): length of memory
batch_size (int): batch size for sampling
target_update (int): period for target model's hard update
lr (float): learning rate
gamma (float): discount factor
alpha (float): determines how much prioritization is used
beta (float): determines how much importance sampling is used
prior_eps (float): guarantees every transition can be sampled
v_min (float): min value of support
v_max (float): max value of support
atom_size (int): the unit number of support
n_step (int): step number to calculate n-step td error
"""
obs_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
self.env = env
self.batch_size = batch_size
self.target_update = target_update
self.gamma = gamma
# NoisyNet: All attributes related to epsilon are removed
#produces a unique timestamp for each run
run_timestamp=str(
#returns number of day and number of month
str(time.localtime(time.time())[2]) + "_" +
str(time.localtime(time.time())[1]) + "_" +
#returns hour, minute and second
str(time.localtime(time.time())[3]) + "_" +
str(time.localtime(time.time())[4]) + "_" +
str(time.localtime(time.time())[5])
)
#Will write scalars that can be visualized using tensorboard in the directory "runLogs/timestamp"
self.writer = SummaryWriter("runLogs/" + run_timestamp)
# device: cpu / gpu
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu"
)
print(self.device)
# PER
# memory for 1-step Learning
self.beta = beta
self.prior_eps = prior_eps
self.memory = PrioritizedReplayBuffer(
memory_size,
{
"obs": {"shape": (obs_dim,)},
"act": {"shape": (1,)},
"rew": {},
"next_obs": {"shape": (obs_dim,)},
"done": {}
},
alpha=alpha
)
# memory for N-step Learning
self.use_n_step = True if n_step > 1 else False
if self.use_n_step:
self.n_step = n_step
self.memory_n = ReplayBuffer(
memory_size,
{
"obs": {"shape": (obs_dim,)},
"act": {"shape": (1,)},
"rew": {},
"next_obs": {"shape": (obs_dim,)},
"done": {}
},
Nstep={
"size": n_step,
"gamma": gamma,
"rew": "rew",
"next": "next_obs"
}
)
# Categorical DQN parameters
self.v_min = v_min
self.v_max = v_max
self.atom_size = atom_size
self.support = torch.linspace(
self.v_min, self.v_max, self.atom_size
).to(self.device)
# networks: dqn, dqn_target
self.dqn = Network(
obs_dim, action_dim, self.atom_size, self.support
).to(self.device)
self.dqn_target = Network(
obs_dim, action_dim, self.atom_size, self.support
).to(self.device)
self.dqn_target.load_state_dict(self.dqn.state_dict())
self.dqn_target.eval()
# optimizer
self.optimizer = optim.Adam(self.dqn.parameters(),0.0001)
# transition to store in memory
self.transition = list()
# mode: train / test
self.is_test = False
# Custom tensorboard object
# self.tensorboard = RainbowTensorBoard(
# log_dir="single_joint_logs/{}-{}".format(
# model_name,
# datetime.now().strftime("%m-%d-%Y-%H_%M_%S")
# )
# )
# Convergence criterion
self.convergence_window = convergence_window
self.convergence_window_epsilon_p = convergence_window_epsilon_p
self.convergence_avg_score = convergence_avg_score
self.convergence_avg_epsilon = convergence_avg_epsilon
self.convergence_avg_epsilon_p = convergence_avg_epsilon_p
def select_action(self, state: np.ndarray) -> np.ndarray:
"""Select an action from the input state."""
# NoisyNet: no epsilon greedy action selection
selected_action = self.dqn(
torch.FloatTensor(state).to(self.device)
).argmax()
selected_action = selected_action.detach().cpu().numpy()
if not self.is_test:
self.transition = [state, selected_action]
return selected_action
def step(self, action: np.ndarray, score:int) -> Tuple[np.ndarray, np.float64, bool]:
"""Take an action and return the response of the env."""
next_state, reward, done, _ = self.env.step(action,score)
if not self.is_test:
self.transition += [reward, next_state, done]
# N-step transition
if self.use_n_step:
idx = self.memory_n.add(
**dict(
zip(["obs", "act", "rew", "next_obs", "done"], self.transition)
)
)
one_step_transition = [ v[idx] for _,v in self.memory_n.get_all_transitions().items()] if idx else None
# 1-step transition
else:
one_step_transition = self.transition
# add a single step transition
if one_step_transition:
self.memory.add(
**dict(
zip(["obs", "act", "rew", "next_obs", "done"], one_step_transition)
)
)
return next_state, reward, done
def update_model(self,frame_idx:int) -> torch.Tensor:
"""Update the model by gradient descent.
shape of elementwise_loss = [128,51]
shape of loss = ([])
shape of weights ([128,1)]
"""
# PER needs beta to calculate weights
samples = self.memory.sample(self.batch_size, beta=self.beta)
weights = torch.FloatTensor(
samples["weights"].reshape(-1, 1)
).to(self.device)
indices = samples["indexes"]
#rospy.loginfo(samples.keys())
#rospy.loginfo(weights.shape)
#rospy.loginfo(indices.shape())
#torch.save(self.dqn.state_dict(),str("checkpoint_"+str(time.time())))
# 1-step Learning loss
elementwise_loss = self._compute_dqn_loss(samples, self.gamma)
# PER: importance sampling before average
loss = torch.mean(elementwise_loss * weights)
self.writer.add_scalar('update_model/Lossv0', loss.detach().item(),frame_idx )
# N-step Learning loss
# we are gonna combine 1-step loss and n-step loss so as to
# prevent high-variance. The original rainbow employs n-step loss only.
if self.use_n_step:
gamma = self.gamma ** self.n_step
samples = {k: [v[i] for i in indices] for k,v in self.memory_n.get_all_transitions().items()}
elementwise_loss_n_loss = self._compute_dqn_loss(samples, gamma)
elementwise_loss += elementwise_loss_n_loss
#rospy.loginfo(elementwise_loss_n_loss.shape)
#rospy.loginfo(elementwise_loss.shape)
# PER: importance sampling before average
loss = torch.mean(elementwise_loss * weights)
rospy.loginfo(
f"{elementwise_loss}"
)
self.optimizer.zero_grad()
self.writer.add_scalar('update_model/Lossv1', loss.detach().item(),frame_idx )
#From pytorch doc: backward() Computes the gradient of current tensor w.r.t. graph leaves.
#self.writer.add_image("loss gradient before", loss, frame_idx)
loss.backward()
#self.writer.add_image("loss gradient after", loss, frame_idx)
self.writer.add_scalar('update_model/Lossv2', loss.detach().item(),frame_idx )
clip_grad_norm_(self.dqn.parameters(), 10.0)
self.optimizer.step()
# PER: update priorities
loss_for_prior = elementwise_loss.detach().cpu().numpy()
new_priorities = loss_for_prior + self.prior_eps
self.memory.update_priorities(indices, new_priorities)
# NoisyNet: reset noise
self.dqn.reset_noise()
self.dqn_target.reset_noise()
#rospy.loginfo("second")
#rospy.loginfo(loss.shape)
#rospy.loginfo("loss dimension = " + loss.ndim() )
#rospy.loginfo("loss = " + str(loss.detach().item()) + "type = " + str(type(loss.detach().item()) ) )
self.writer.add_scalar('update_model/Loss', loss.detach().item(),frame_idx )
return loss.detach().item()
def train(self, num_frames: int):
"""Train the agent."""
self.is_test = False
state = self.env.reset()
update_cnt = 0
losses = []
scores = []
score = 0
for frame_idx in tqdm(range(1, num_frames + 1)):
action = self.select_action(state)
next_state, reward, done = self.step(action,score)
state = next_state
score += reward
# NoisyNet: removed decrease of epsilon
# PER: increase beta
fraction = min(frame_idx / num_frames, 1.0)
self.beta = self.beta + fraction * (1.0 - self.beta)
# if episode ends
if done:
#rospy.loginfo("logging for done")
self.writer.add_scalar('train/score', score, frame_idx)
self.writer.add_scalar('train/final_epsilon', state[6], frame_idx)
self.writer.add_scalar('train/epsilon_p', state[7], frame_idx)
state = self.env.reset()
scores.append(score)
score = 0
# if training is ready
if self.memory.get_stored_size() >= self.batch_size:
#frame_id given as argument for logging by self.writer.
#rospy.loginfo("frame_idx= " + str(frame_idx) + "type = " + str(type(frame_idx)))
loss = self.update_model(frame_idx)
losses.append(loss)
update_cnt += 1
# if hard update is needed
if update_cnt % self.target_update == 0:
self._target_hard_update(loss)
self.env.close()
def test(self) -> List[np.ndarray]:
"""Test the agent."""
self.is_test = True
state = self.env.reset()
done = False
score = 0
frames = []
while not done:
frames.append(self.env.render(mode="rgb_array"))
action = self.select_action(state)
next_state, reward, done = self.step(action)
state = next_state
score += reward
print("score: ", score)
self.env.close()
return frames
def _compute_dqn_loss(self, samples: Dict[str, np.ndarray], gamma: float) -> torch.Tensor:
"""Return categorical dqn loss."""
device = self.device # for shortening the following lines
state = torch.FloatTensor(samples["obs"]).to(device)
next_state = torch.FloatTensor(samples["next_obs"]).to(device)
action = torch.LongTensor(samples["act"]).to(device)
reward = torch.FloatTensor(np.array(samples["rew"]).reshape(-1, 1)).to(device)
done = torch.FloatTensor(np.array(samples["done"]).reshape(-1, 1)).to(device)
# Categorical DQN algorithm
delta_z = float(self.v_max - self.v_min) / (self.atom_size - 1)
with torch.no_grad():
# Double DQN
next_action = self.dqn(next_state).argmax(1)
next_dist = self.dqn_target.dist(next_state)
next_dist = next_dist[range(self.batch_size), next_action]
t_z = reward + (1 - done) * gamma * self.support
t_z = t_z.clamp(min=self.v_min, max=self.v_max)
b = (t_z - self.v_min) / delta_z
l = b.floor().long()
u = b.ceil().long()
offset = (
torch.linspace(
0, (self.batch_size - 1) * self.atom_size, self.batch_size
).long()
.unsqueeze(1)
.expand(self.batch_size, self.atom_size)
.to(self.device)
)
proj_dist = torch.zeros(next_dist.size(), device=self.device)
proj_dist.view(-1).index_add_(
0, (l + offset).view(-1), (next_dist * (u.float() - b)).view(-1)
)
proj_dist.view(-1).index_add_(
0, (u + offset).view(-1), (next_dist * (b - l.float())).view(-1)
)
print(f"Next Action : {next_action}\n Next Dist : {next_dist}\n")
dist = self.dqn.dist(state)
log_p = torch.log(dist[range(self.batch_size), action])
elementwise_loss = -(proj_dist * log_p).sum(1)
print(f"Proj Dist : {proj_dist}\n Dist : {dist}\n Log_p : {log_p}\n")
if torch.isnan(elementwise_loss[0][0]):
exit()
return elementwise_loss
def _target_hard_update(self,loss):
"""Hard update: target <- local."""
self.dqn_target.load_state_dict(self.dqn.state_dict())
#torch.save(self.dqn.state_dict(),str("checkpoint_"+str(time.time())))
torch.save({
'model_state_dict': self.dqn.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict(),
'loss': loss,
}, str("checkpoints/checkpoint_"+str(time.time())))
if np.random.rand() < egreedy:
action = env.action_space.sample()
else:
Q = tf.squeeze(model(observation.reshape(1, -1)))
action = np.argmax(Q)
next_observation, reward, done, info = env.step(action)
rb.add(obs=observation,
act=action,
rew=reward,
next_obs=next_observation,
done=done)
observation = next_observation
if prioritized:
sample = rb.sample(batch_size, beta)
beta += beta_step
else:
sample = rb.sample(batch_size)
weights = sample["weights"].ravel() if prioritized else tf.constant(1.0)
with tf.GradientTape() as tape:
tape.watch(model.trainable_weights)
Q = Q_func(model, tf.constant(sample["obs"]),
tf.constant(sample["act"].ravel()),
tf.constant(env.action_space.n))
target_Q = target_func(model, target_model,
tf.constant(sample['next_obs']),
tf.constant(sample["rew"].ravel()),
tf.constant(sample["done"].ravel()), discount,
r = np.random.rand(1)
d = np.random.randint(2) # [0,2) == 0 or 1
p = np.random.rand(1)
client.insert([o, a, r, o, d], priorities={"PrioritizedReplayBuffer": p})
prb.add(obs=o, act=a, rew=r, next_obs=o, done=d, priority=p)
perfplot.plot(time_unit="ms",
setup=lambda n: n,
kernels=[
sample_client(client, "PrioritizedReplayBuffer"),
sample_tf_client(tf_client, "PrioritizedReplayBuffer"),
sample_tf_client_dataset(tf_client,
"PrioritizedReplayBuffer"),
lambda n: prb.sample(n, beta=beta)
],
labels=[
"DeepMind/Reverb: Client.sample",
"DeepMind/Reverb: TFClient.sample",
"DeepMind/Reverb: TFClient.dataset", "cpprb"
],
n_range=[2**n for n in range(1, 9)],
xlabel="Batch size",
logx=False,
logy=False,
equality_check=None)
plt.title("Prioritized Replay Buffer Sample Speed")
plt.savefig("PrioritizedReplayBuffer_sample2.png",
transparent=True,
bbox_inches="tight")
class Agent:
def __init__(self,
lr,
state_shape,
num_actions,
batch_size,
max_mem_size=100000):
self.lr = lr
self.gamma = 0.99
self.action_space = list(range(num_actions))
self.batch_size = batch_size
self.epsilon = Lerper(start=1.0, end=0.01, num_steps=2000)
self.importance_exp = Lerper(start=0.4, end=1.0, num_steps=100000)
self.priority_exp = 0.6
self.memory = PrioritizedReplayBuffer(max_mem_size, {
"obs": {
"shape": state_shape
},
"act": {
"shape": 1
},
"rew": {},
"next_obs": {
"shape": state_shape
},
"done": {
"shape": 1
}
},
alpha=self.priority_exp)
self.net = Network(lr, state_shape, num_actions)
def choose_action(self, observation):
if np.random.random() > self.epsilon.value():
state = torch.tensor(observation).float().detach()
state = state.to(self.net.device)
state = state.unsqueeze(0)
q_values = self.net(state)
action = torch.argmax(q_values).item()
return action
else:
return np.random.choice(self.action_space)
def store_memory(self, state, action, reward, next_state, done):
self.memory.add(obs=state,
act=action,
rew=reward,
next_obs=next_state,
done=done)
def learn(self):
if self.memory.get_stored_size() < self.batch_size:
return
batch = self.memory.sample(self.batch_size,
self.importance_exp.value())
states = torch.tensor(batch["obs"]).to(self.net.device)
actions = torch.tensor(batch["act"],
dtype=torch.int64).to(self.net.device).T[0]
rewards = torch.tensor(batch["rew"]).to(self.net.device).T[0]
states_ = torch.tensor(batch["next_obs"]).to(self.net.device)
dones = torch.tensor(batch["done"],
dtype=torch.bool).to(self.net.device).T[0]
weights = torch.tensor(batch["weights"]).to(self.net.device)
batch_index = np.arange(self.batch_size, dtype=np.int64)
q_values = self.net(states)[batch_index, actions]
q_values_ = self.net(states_)
action_qs_ = torch.max(q_values_, dim=1)[0]
action_qs_[dones] = 0.0
q_target = rewards + self.gamma * action_qs_
td = q_target - q_values
self.net.optimizer.zero_grad()
loss = ((td**2.0) * weights).mean()
loss.backward()
self.net.optimizer.step()
new_priorities = (td.abs()).detach().cpu()
self.memory.update_priorities(batch["indexes"], new_priorities)
self.epsilon.step()
self.importance_exp.step()
if int(mean) > best_reward:
best_reward = int(mean)
torch.save(net.state_dict(), f=os.path.join(
folder, sub_folder, str(best_reward) + '.dat'))
if mean > params.solve_rewards:
print('Solved in {}!'.format(datetime.now()-st))
if args.play:
utils.play(params.env, agent, wait=0.01)
break
if frame < params.init_replay:
continue
if frame % args.envs == 0:
if args.priority:
batch = buffer.sample(params.batch_size, BETA)
BETA = min(BETA + frame * step, TGT_BETA)
else:
batch = buffer.sample(params.batch_size)
optimizer.zero_grad()
if args.priority:
loss_v, batch_prios, batch_indexes = calc_loss_dqn(batch, net,
tgt_net, params.gamma, device, True)
else:
loss_v = calc_loss_dqn(
batch, net, tgt_net, params.gamma**args.steps, device=device)
loss_v.backward()
optimizer.step()
if args.priority:
buffer.update_priorities(batch_indexes, batch_prios)
class ReplayMemory():
def __init__(self, args, capacity, env):
# Initial importance sampling weight β, annealed to 1 over course of training
self.priority_weight = args.priority_weight
self.n = args.multi_step
self.device = args.device
if args.mmap:
os.makedirs('memories/', exist_ok=True)
mmap_prefix = 'memories/mm'
else:
mmap_prefix = None
self.buffer = PrioritizedReplayBuffer(
capacity,
{
"obs": {
"shape": env.observation_space.shape,
"dtype": env.observation_space.dtype
},
"next_obs": {
"shape": env.observation_space.shape,
"dtype": env.observation_space.dtype
},
"act": {
"shape": 1,
"dtype": env.action_space.dtype
},
"rew": {
"dtype": np.float32
},
"done": {
"dtype": np.uint8
},
},
Nstep={
"size": self.n,
"gamma": args.discount,
"rew": "rew",
"next": "next_obs",
},
mmap_prefix=mmap_prefix,
alpha=args.priority_exponent,
# next_of="obs",
# stack_compress="obs",
)
def append(self, state, next_state, action, reward, done):
self.buffer.add(
**{
"obs": state,
"next_obs": next_state,
"act": action,
"rew": reward,
"done": done,
})
def sample(self, size):
s = self.buffer.sample(size, self.priority_weight)
s['indexes'] = s['indexes'].astype(np.int32)
return torchify((s['indexes'], torch.int32), (s['obs'], torch.float32),
(np.squeeze(s['act'], 1), torch.long),
(np.squeeze(s['rew'], 1), torch.float32),
(s['next_obs'], torch.float32),
(s['done'], torch.bool), (s['weights'], torch.float32),
device=self.device)
def update_priorities(self, indexes, new_priorities):
indexes = indexes.cpu().numpy()
self.buffer.update_priorities(indexes, new_priorities)
class RainbowAgent:
"""
Rainbow Agent interacting with environment.
Attribute:
env (gym.Env): openAI Gym environment (connected to Gazebo node)
memory (PrioritizedReplayBuffer): replay memory to store transitions
batch_size (int): batch size for sampling
target_update (int): period for target model's hard update
gamma (float): discount factor
dqn (Network): model to train and select actions
dqn_target (Network): target model to update
optimizer (torch.optim): optimizer for training dqn
transition (list): transition information including
state, action, reward, next_state, done
v_min (float): min value of support
v_max (float): max value of support
atom_size (int): the unit number of support
support (torch.Tensor): support for categorical dqn
use_n_step (bool): whether to use n_step memory
n_step (int): step number to calculate n-step td error
memory_n (ReplayBuffer): n-step replay buffer
"""
def __init__(
self,
env: gym.Env,
memory_size: int,
batch_size: int,
target_update: int,
gamma: float = 0.99,
# PER parameters
alpha: float = 0.2,
beta: float = 0.6,
prior_eps: float = 1e-6,
# Categorical DQN parameters
v_min: float = 0.0,
v_max: float = 200.0,
atom_size: int = 51,
# N-step Learning
n_step: int = 3,
# Convergence parameters
convergence_window: int = 100,
convergence_window_epsilon_p: int = 10,
convergence_avg_score: float = 195.0,
convergence_avg_epsilon: float = 0.0524, # 3 degs converted to rads
convergence_avg_epsilon_p: float = 0.0174, # 1 deg/s converted to rad/s
# Tensorboard parameters
model_name: str = "snake_joint",
):
"""
Initialization.
Args:
env_client (GymEnvClient): ROS client to an openAI Gym environment server
memory_size (int): length of memory
batch_size (int): batch size for sampling
target_update (int): period for target model's hard update
lr (float): learning rate
gamma (float): discount factor
alpha (float): determines how much prioritization is used
beta (float): determines how much importance sampling is used
prior_eps (float): guarantees every transition can be sampled
v_min (float): min value of support
v_max (float): max value of support
atom_size (int): the unit number of support
n_step (int): step number to calculate n-step td error
"""
obs_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
self.env = env
self.batch_size = batch_size
self.target_update = target_update
self.gamma = gamma
# Selecting computing device
physical_devices = tf.config.list_physical_devices('GPU')
n_gpu = len(physical_devices)
rospy.loginfo("Number of GPU detected : " + str(n_gpu))
if n_gpu > 0:
rospy.loginfo("Switching to single GPU mode : /device:GPU:0")
self.used_device = "/device:GPU:0"
tf.config.experimental.set_memory_growth(physical_devices[0], True)
else:
rospy.loginfo("No GPU detected. Switching to single CPU mode : /device:CPU:0")
self.used_device = "/device:CPU:0"
# PER
# memory for 1-step learning
self.beta = beta
self.prior_eps = prior_eps
self.memory = PrioritizedReplayBuffer(
memory_size,
{
"obs": {"shape": (obs_dim,)},
"act": {"shape": (1,)},
"rew": {},
"next_obs": {"shape": (obs_dim,)},
"done": {}
},
alpha=alpha
)
# memory for N-step learning
self.use_n_step = True if n_step > 1 else False
if self.use_n_step:
self.n_step = n_step
self.memory_n = ReplayBuffer(
memory_size,
{
"obs": {"shape": (obs_dim,)},
"act": {"shape": (1,)},
"rew": {},
"next_obs": {"shape": (obs_dim,)},
"done": {}
},
Nstep={
"size": n_step,
"gamma": gamma,
"rew": "rew",
"next": "next_obs"
}
)
# Categorical DQN parameters
self.v_min = v_min
self.v_max = v_max
self.atom_size = atom_size
self.support = tf.linspace(self.v_min, self.v_max, self.atom_size, name="support")
# networks: dqn, dqn_target
self.dqn = Network(
obs_dim, action_dim, self.atom_size, self.support, name="dqn"
)
self.dqn_target = Network(
obs_dim, action_dim, self.atom_size, self.support, name="dqn_target"
)
# optimizer
self.optimizer = Adam(
learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False, name='AdamOptimizer'
)
# transition to store in memory
self.transition = list()
# mode: train / test
self.is_test = False
# Custom tensorboard object
self.tensorboard = RainbowTensorBoard(
log_dir="single_joint_logs/{}-{}".format(
model_name,
datetime.now().strftime("%m-%d-%Y-%H_%M_%S")
)
)
# Convergence criterion
self.convergence_window = convergence_window
self.convergence_window_epsilon_p = convergence_window_epsilon_p
self.convergence_avg_score = convergence_avg_score
self.convergence_avg_epsilon = convergence_avg_epsilon
self.convergence_avg_epsilon_p = convergence_avg_epsilon_p
#TODO
# model checkpoint object
self.checkpoint = tf.train.Checkpoint(optimizer=self.optimizer, model=self.dqn_target)
self.checkpoint_manager = tf.train.CheckpointManager(
self.checkpoint, directory="single_joint_ckpts", max_to_keep=5
)
def select_action(self, state: np.ndarray) -> np.ndarray:
"""Select an action from the input state."""
# NoisyNet: no epsilon greedy action selection
selected_action = tf.math.argmax(self.dqn(
tf.constant(state.reshape(1, state.shape[0]), dtype=tf.float32)
), axis=-1, name="argmax_selected_action")
# Convert to numpy ndarray datatype
selected_action = selected_action.numpy()
if not self.is_test:
self.transition = [state, selected_action]
return selected_action
def step(self, action: np.ndarray) -> Tuple[np.ndarray, np.float64, bool]:
"""
Take an action and return the response of the env.
"""
next_state, reward, done, _ = self.env.step(action,score)
if not self.is_test:
self.transition += [reward, next_state, done]
# N-step transition
if self.use_n_step:
idx = self.memory_n.add(
**dict(
zip(["obs", "act", "rew", "next_obs", "done"], self.transition)
)
)
one_step_transition = [ v[idx] for _,v in self.memory_n.get_all_transitions().items()] if idx else None
# 1-step transition
else:
one_step_transition = self.transition
# add a single step transition
if one_step_transition:
self.memory.add(
**dict(
zip(["obs", "act", "rew", "next_obs", "done"], one_step_transition)
)
)
return next_state, reward, done
def update_model(self) -> tf.Tensor:
"""
Update the model by gradient descent
"""
# PER needs beta to calculate weights
samples = self.memory.sample(self.batch_size, beta=self.beta)
weights = tf.constant(
samples["weights"].reshape(-1, 1),
dtype=tf.float32,
name="update_model_weights"
)
indices = samples["indexes"]
# 1-step Learning loss
elementwise_loss = self._compute_dqn_loss(samples, self.gamma)
with tf.GradientTape() as tape:
# PER: importance of sampling before average
loss = tf.math.reduce_mean(elementwise_loss * weights)
# N-step Learning loss
# We are going to combine 1-ste[ loss and n-step loss so as to
# prevent high-variance.
if self.use_n_step:
gamma = self.gamma ** self.n_step
samples = {k: [v[i] for i in indices] for k,v in self.memory_n.get_all_transitions().items()}
elementwise_loss_n_loss = self._compute_dqn_loss(samples, gamma)
elementwise_loss += elementwise_loss_n_loss
# PER: importance of sampling before average
loss = tf.math.reduce_mean(elementwise_loss * weights)
dqn_variables = self.dqn.trainable_variables
gradients = tape.gradient(loss, dqn_variables)
gradients, _ = tf.clip_by_global_norm(gradients, 10.0)
self.optimizer.apply_gradients(zip(gradients, dqn_variables))
# PER: update priorities
loss_for_prior = elementwise_loss.numpy()
new_priorities = loss_for_prior + self.prior_eps
self.memory.update_priorities(indices, new_priorities)
# NoisyNet: reset noise
self.dqn.reset_noise()
self.dqn_target.reset_noise()
return loss.numpy().ravel()
def train(self, num_frames: int):
"""Train the agent."""
self.is_test = False
state = self.env.reset()
update_cnt = 0
scores = deque(maxlen=self.convergence_window)
joint_epsilon = deque(maxlen=self.convergence_window)
joint_epsilon_p = deque(maxlen=self.convergence_window_epsilon_p)
score = 0 # cumulated reward
episode_length = 0
episode_cnt = 0
for frame_idx in tqdm(range(1, num_frames + 1), file=tqdm_out):
action = self.select_action(state)
next_state, reward, done = self.step(action)
state = next_state
score += reward
episode_length += 1
# PER: increase beta
fraction = min(frame_idx / num_frames, 1.0)
self.beta = self.beta + fraction * (1.0 - self.beta)
print("epsilon_p is {}".format(state[7]))
print("epsilon is {}".format(state[6]))
if done:
print("done")
# to be used for convergence criterion
scores.append(score)
joint_epsilon.append(state[6])
joint_epsilon_p.append(state[7])
#
state = self.env.reset()
self.tensorboard.update_stats(
score={
"data": score,
"desc": "Score (or cumulated rewards) for an episode - episode index on x-axis."
},
episode_length={
"data": episode_length,
"desc": "Episode length (in frames)"
},
final_epsilon={
"data": state[6],
"desc": "Value of epsilon = abs(theta_ld - theta_l) at the last frame of an episode"
},
final_epsilon_p={
"data": state[7],
"desc": "Value of d(epsilon)/dt at the last frame of an episode"
}
)
score = 0
episode_length = 0
episode_cnt += 1
# check convergence criterion
converged = bool(
len(scores) == self.convergence_window and # be sure the score buffer is full
len(joint_epsilon) == self.convergence_window and # same for epsilon buffer
len(joint_epsilon_p) == self.convergence_window and # same for epsilon_p buffer
mean(scores) > self.convergence_avg_score and
mean(joint_epsilon) < self.convergence_avg_epsilon and
mean(joint_epsilon_p) < self.convergence_avg_epsilon_p
)
if converged:
rospy.loginfo("Ran {} episodes. Solved after {} trials".format(episode_cnt, frame_idx))
return
# if training is ready
if self.memory.get_stored_size() >= self.batch_size:
loss = self.update_model()
# plotting loss every frame
self.tensorboard.update_stats(
loss={
"data": loss[0],
"desc": "Loss value."
}
)
update_cnt += 1
# if hard update is needed
if update_cnt % self.target_update == 0:
self._target_hard_update()
# checkpointing of target model (only if the loss decrease)
self.checkpoint_manager.save()
self.env.close()
def test(self) -> List[np.ndarray]:
"""Test the agent."""
self.is_test = True
state = self.env.reset()
done = False
score = 0
frames = []
while not done:
frames.append(self.env.render(mode="rgb_array"))
action = self.select_action(state)
next_state, reward, done = self.step(action)
state = next_state
score += reward
rospy.loginfo("score: ", score)
self.env.close()
return frames
def _compute_dqn_loss(self, samples: Dict[str, np.ndarray], gamma: float) -> tf.Tensor:
with tf.device(self.used_device):
state = tf.constant(samples["obs"], dtype=tf.float32)
next_state = tf.constant(samples["next_obs"], dtype=tf.float32)
action = tf.constant(samples["act"], dtype=tf.float32)
reward = tf.reshape(tf.constant(samples["rew"], dtype=tf.float32), [-1, 1])
done = tf.reshape(tf.constant(samples["done"], dtype=tf.float32), [-1, 1])
# Categorical DQN algorithm
delta_z = float(self.v_max - self.v_min) / (self.atom_size - 1)
# Double DQN
next_action = tf.math.argmax(self.dqn(next_state), axis=1)
next_dist = self.dqn_target.dist(next_state)
next_dist = tf.gather_nd(
next_dist,
[[i, next_action.numpy()[0]] for i in range(self.batch_size)]
)
t_z = reward + (1 - done) * gamma * self.support
t_z = tf.clip_by_value(t_z, clip_value_min=self.v_min, clip_value_max=self.v_max)
b = tf.dtypes.cast((t_z - self.v_min) / delta_z, tf.float64)
l = tf.dtypes.cast(tf.math.floor(b), tf.float64)
u = tf.dtypes.cast(tf.math.ceil(b), tf.float64)
offset = (
tf.broadcast_to(
tf.expand_dims(
tf.dtypes.cast(
tf.linspace(0, (self.batch_size - 1) * self.atom_size, self.batch_size),
tf.float64
),
axis=1
),
[self.batch_size, self.atom_size]
)
)
proj_dist = tf.zeros(tf.shape(next_dist), tf.float64)
# casting
next_dist = tf.dtypes.cast(next_dist, tf.float64)
proj_dist = tf.tensor_scatter_nd_add(
tf.reshape(proj_dist, [-1]), # input tensor
tf.reshape(tf.dtypes.cast(l + offset, tf.int64), [-1, 1]), # indices
tf.reshape((next_dist * (u - b)), [-1]) # updates
)
proj_dist = tf.tensor_scatter_nd_add(
proj_dist,
tf.reshape(tf.dtypes.cast(u + offset, tf.int64), [-1, 1]), # indices
tf.reshape((next_dist * (b - l)), [-1]) # updates
)
proj_dist = tf.reshape(proj_dist, [self.batch_size, self.atom_size])
dist = self.dqn.dist(state)
#log_p = tf.math.log(dist[range(self.batch_size), action])
log_p = tf.dtypes.cast(
tf.math.log(
tf.gather_nd(
dist,
[[i, tf.dtypes.cast(tf.reshape(action, [-1]), tf.int32).numpy()[i]] for i in range(self.batch_size)]
)
),
tf.float64
)
elementwise_loss = tf.math.reduce_sum(-(proj_dist * log_p), axis=1)
return tf.dtypes.cast(elementwise_loss, tf.float32)
def _target_hard_update(self):
"""Hard update: target <- local."""
tf.saved_model.save(self.dqn, "single_joint_dqn")
self.dqn_target = tf.saved_model.load("single_joint_dqn")
class DQNAgent:
def __init__(self):
# other hyperparameters
self.save_graph = True
self.isTraining = True
self.keepTraining = False
self.play = False
self.render = False
self.save_model = True
self.load_model = False
self.random = False
self.dueling = True
# epsilon greedy exploration
self.initial_epsilon = 1.0
self.epsilon = self.initial_epsilon
self.min_epsilon = 0.01
self.linear_annealed = (self.initial_epsilon - self.min_epsilon) / 2000
self.decay_rate = 0.995
# check the hyperparameters
if self.random:
self.play = False
self.isTraining = False
if self.play:
self.render = True
self.save_model = False
self.load_model = True
self.isTraining = False
self.keepTraining = False
if self.keepTraining:
self.epsilon = self.min_epsilon
self.load_model = True
# fixed q value - two networks
self.learning_rate = 0.0001
self.fixed_q_value_steps = 100
self.target_network_counter = 0
# n-step learning
self.n_step = 3
self.n_step_buffer = deque(maxlen=self.n_step)
# experience replay used SumTree
# combine agent and PER
self.batch_size = 64
self.gamma = 0.9
self.replay_start_size = 320
self.PER_e = 0.01 # epsilon -> pi = |delta| + epsilon transitions which have zero error also have chance to be selected
self.PER_a = 0.6 # P(i) = p(i) ** a / total_priority ** a
self.PER_b = 0.4
self.PER_b_increment = 0.005
self.absolute_error_upper = 1. # clipped error
self.experience_number = 0
env_dict = {
"obs": {
"shape": (state_size, )
},
"act": {},
"rew": {},
"next_obs": {
"shape": (state_size, )
},
"done": {}
}
self.experience_replay = PrioritizedReplayBuffer(memory_size,
env_dict=env_dict,
alpha=self.PER_a,
eps=self.PER_e)
# initially, p1=1 total_priority=1,so P(1)=1,w1=batchsize**beta
if self.load_model:
self.model = keras.models.load_model('cartpole_nstep.h5')
self.target_model = keras.models.load_model('cartpole_nstep.h5')
else:
self.model = self.create_model()
self.target_model = self.create_model()
# n-step learning, get the truncated n-step return
def get_n_step_info(self, n_step_buffer, gamma):
"""Return n step reward, next state, and done."""
# info of the last transition
reward, next_state, done = n_step_buffer[-1][-3:]
for transition in reversed(list(n_step_buffer)[:-1]):
r, n_s, d = transition[-3:]
reward = r + gamma * reward * (1 - d)
next_state, done = (n_s, d) if d else (next_state, done)
return reward, next_state, done
def store(self, experience):
self.n_step_buffer.append(experience)
if len(self.n_step_buffer) == self.n_step:
reward, next_state, done = self.get_n_step_info(
self.n_step_buffer, self.gamma)
state, action = self.n_step_buffer[0][:2]
self.experience_replay.add(obs=state,
act=action,
rew=reward,
next_obs=next_state,
done=done)
def create_model(self):
inputs = tf.keras.Input(shape=(state_size, ))
fc1 = tf.keras.layers.Dense(128, activation='relu')(inputs)
fc2 = tf.keras.layers.Dense(128, activation='relu')(fc1)
advantage_output = tf.keras.layers.Dense(action_size,
activation='linear')(fc2)
value_out = tf.keras.layers.Dense(1, activation='linear')(fc2)
norm_advantage_output = tf.keras.layers.Lambda(
lambda x: x - tf.reduce_mean(x))(advantage_output)
outputs = tf.keras.layers.Add()([value_out, norm_advantage_output])
model = tf.keras.Model(inputs, outputs)
model.compile(optimizer=tf.keras.optimizers.Adam(self.learning_rate),
loss=tf.keras.losses.MeanSquaredError(),
metrics=['accuracy'])
model.summary()
return model
def train(self):
if self.experience_replay.get_stored_size() > self.batch_size:
samples = self.experience_replay.sample(self.batch_size)
td_errors, loss = self._train_body(samples)
self.experience_replay.update_priorities(samples["indexes"],
td_errors.numpy() + 1e-6)
@tf.function
def _train_body(self, samples):
with tf.GradientTape() as tape:
td_errors = self._compute_td_error_body(samples["obs"],
samples["act"],
samples["rew"],
samples["next_obs"],
samples["done"])
loss = tf.reduce_mean(
tf.square(td_errors)) # huber loss seems no use
gradients = tape.gradient(loss, self.model.trainable_variables)
self.model.optimizer.apply_gradients(
zip(gradients, self.model.trainable_variables))
return td_errors, loss
@tf.function
def _compute_td_error_body(self, states, actions, rewards, next_states,
dones):
rewards = tf.cast(tf.squeeze(rewards), dtype=tf.float32)
dones = tf.cast(tf.squeeze(dones), dtype=tf.bool)
actions = tf.cast(actions, dtype=tf.int32) # (batch_size, 1)
batch_size_range = tf.expand_dims(tf.range(self.batch_size),
axis=1) # (batch_size, 1)
# get current q value
current_q_indexes = tf.concat(values=(batch_size_range, actions),
axis=1) # (batch_size, 2)
current_q = tf.gather_nd(self.model(states),
current_q_indexes) # (batch_size, )
# get target q value using double dqn
max_next_q_indexes = tf.argmax(self.model(next_states),
axis=1,
output_type=tf.int32) # (batch_size, )
indexes = tf.concat(values=(batch_size_range,
tf.expand_dims(max_next_q_indexes,
axis=1)),
axis=1) # (batch_size, 2)
target_q = tf.gather_nd(self.target_model(next_states),
indexes) # (batch_size, )
target_q = tf.where(dones, rewards,
rewards + self.gamma * target_q) # (batch_size, )
# don't want change the weights of target network in backpropagation, so tf.stop_gradient()
# but seems no use
td_errors = tf.abs(current_q - tf.stop_gradient(target_q))
return td_errors
def select_action(self, state):
self.target_network_counter += 1
if self.target_network_counter % self.fixed_q_value_steps == 0:
self.target_model.set_weights(self.model.get_weights())
self.epsilon = max(self.epsilon - self.linear_annealed,
self.min_epsilon)
if np.random.sample() <= self.epsilon:
return np.random.randint(action_size)
return self._get_action_body(state).numpy()
@tf.function
def _get_action_body(self, state):
state = tf.expand_dims(state, axis=0)
qvalues = self.model(state)[0]
return tf.argmax(qvalues)
if np.random.rand() < egreedy:
action = env.action_space.sample()
else:
Q = tf.squeeze(model(observation.reshape(1,-1)))
action = np.argmax(Q)
next_observation, reward, done, info = env.step(action)
rb.add(obs=observation,
act=action,
rew=reward,
next_obs=next_observation,
done=done)
observation = next_observation
sample = rb.sample(batch_size,beta=0.0)
with tf.GradientTape() as tape:
tape.watch(model.trainable_weights)
Q = Q_func(model,
tf.constant(sample["obs"]),
tf.constant(sample["act"].ravel()),
tf.constant(env.action_space.n))
target_Q = target_func(model,target_model,
tf.constant(sample['next_obs']),
tf.constant(sample["rew"].ravel()),
tf.constant(sample["done"].ravel()),
discount,
tf.constant(env.action_space.n))
absTD = tf.math.abs(target_Q - Q)
loss = tf.reduce_mean(loss_func(absTD))
class ReplayBuffer():
def __init__(self, args):
#self.memory = deque(maxlen=args.buffer_size)
self.memory = PrioritizedReplayBuffer(
args.buffer_size, {
"obs": {
"shape": (64, 64, 6)
},
"act": {},
"rew": {},
"next_obs": {
"shape": (64, 64, 6)
},
"terminal": {}
})
#self.priority = deque(maxlen=args.buffer_size)
self.length = 0
self.args = args
def load_queues(self, queues, q_network, target_network, lock, args):
for q in queues:
for i in range(int(q.qsize())):
# Read from the queue
# The critical section begins
lock.acquire()
data = queue_to_data(q.get())
lock.release()
# Convert to numpy for storage
state = data[0].numpy()
action = data[1].numpy()
reward = data[2].numpy()
next_state = data[3].numpy()
terminal = data[4].numpy()
#data_np = (state,action,reward,next_state,terminal)
# Push to the buffer
#self.memory.append(data_np)
self.memory.add(obs=state,
act=action,
rew=reward,
next_obs=next_state,
terminal=terminal)
self.length = min(self.args.buffer_size, self.length + 1)
def prepare_batch(self, target_network, q_network):
batch_size = min(self.length, self.args.batch_size)
sample = self.memory.sample(batch_size)
s = t.tensor(sample['obs'])
a = t.tensor(sample['act'])
r = t.tensor(sample['rew'])
ns = t.tensor(sample['next_obs'])
term = t.tensor(sample['terminal'])
states = s.permute(0, 3, 1, 2).to(Device.get_device())
actions = a.type(t.int64).to(Device.get_device())
rewards = r.to(Device.get_device())
next_states = ns.permute(0, 3, 1, 2).to(Device.get_device())
terminals = term.to(Device.get_device())
indexes = sample["indexes"]
with t.no_grad():
target = rewards + terminals * self.args.gamma * target_network(
next_states).max()
predicted = q_network(states).gather(1, actions)
new_priorities = f.smooth_l1_loss(predicted, target,
reduction='none').cpu().numpy()
new_priorities[new_priorities < 1] = 1
self.memory.update_priorities(indexes, new_priorities)
return states, actions, rewards, next_states, terminals
def __len__(self):
return self.length
