def __init__(self, eps, lr, gamma, batch_size, tau, max_memory, lambda_1,
lambda_2, lambda_3, n_steps, l_margin):
# Input Parameters
self.eps = eps # eps-greedy
self.gamma = gamma # discount factor
self.batch_size = batch_size
self.tau = tau # frequency of target replacement
self.ed = 0.005 # bonus for demonstration # todo they aren't used
self.ea = 0.001 # todo they aren't used
self.l_margin = l_margin
self.n_steps = n_steps
self.lambda1 = lambda_1 # n-step return
self.lambda2 = lambda_2 # supervised loss
self.lambda3 = lambda_3 # L2
self.counter = 0 # target replacement counter # todo change to iter_counter
self.replay = Memory(capacity=max_memory)
self.loss = nn.MSELoss()
self.policy = Policy() # todo change not have to pass architecture
self.opt = optim.Adam(self.policy.predictNet.parameters(),
lr=lr,
weight_decay=lambda_3)
self.replay.e = 0
self.demoReplay = ddict(list)
self.noisy = hasattr(self.policy.predictNet, "sample")
def __init__(self):
self.network, self.target_network = AtariNet(ACTIONS_SIZE), AtariNet(
ACTIONS_SIZE)
self.memory = Memory(MEMORY_SIZE)
self.learning_count = 0
self.optimizer = torch.optim.Adam(self.network.parameters(), lr=LR)
self.loss_func = nn.MSELoss()
class ReplayBuffer:
"""Fixed-size buffer to store experience tuples."""
def __init__(self, action_size, buffer_size, batch_size, seed):
"""Initialize a ReplayBuffer object.
Params
======
action_size (int): dimension of each action
buffer_size (int): maximum size of buffer
batch_size (int): size of each training batch
seed (int): random seed
"""
self.action_size = action_size
self.memory = Memory(capacity=buffer_size,
replay_beta=REPLAY_BETA,
replay_alpha=REPLAY_ALPHA,
replay_beta_increment=REPLAY_BETA_INCREMENT)
self.batch_size = batch_size
self.seed = random.seed(seed)
self.experience = namedtuple(
"Experience",
field_names=["state", "action", "reward", "next_state", "done"])
def add(self, state, action, reward, next_state, done):
"""Add a new experience to memory."""
if len(self.memory) <= self.batch_size:
error = random.random()
else:
error = self.memory.max_prio
e = self.experience(state, action, reward, next_state, done)
self.memory.add(error, e)
def sample(self):
"""Randomly sample a batch of experiences from memory."""
experiences, idxs, ws = self.memory.sample(n=self.batch_size)
states = torch.from_numpy(
np.vstack([e.state for e in experiences
if e is not None])).float().to(device)
actions = torch.from_numpy(
np.vstack([e.action for e in experiences
if e is not None])).long().to(device)
rewards = torch.from_numpy(
np.vstack([e.reward for e in experiences
if e is not None])).float().to(device)
next_states = torch.from_numpy(
np.vstack([e.next_state for e in experiences
if e is not None])).float().to(device)
dones = torch.from_numpy(
np.vstack([e.done for e in experiences
if e is not None]).astype(np.uint8)).float().to(device)
return (states, actions, rewards, next_states, dones), idxs, ws
def __len__(self):
"""Return the current size of internal memory."""
return len(self.memory)
def __init__(self, replay_size, memory_size=10000, prioritized=False):
self.step = 0
self.replay_size = replay_size
self.replay_queue = deque(maxlen=self.replay_size)
self.memory_size = memory_size
self.model = self.create_model()
self.prioritized = prioritized
if self.prioritized:
self.memory = Memory(capacity=memory_size)
class Agent(object):
def __init__(self):
self.network, self.target_network = AtariNet(ACTIONS_SIZE), AtariNet(
ACTIONS_SIZE)
self.memory = Memory(MEMORY_SIZE)
self.learning_count = 0
self.optimizer = torch.optim.Adam(self.network.parameters(), lr=LR)
self.loss_func = nn.MSELoss()
def action(self, state, israndom):
if israndom and random.random() < EPSILON:
return np.random.randint(0, ACTIONS_SIZE)
state = torch.unsqueeze(torch.FloatTensor(state), 0)
actions_value = self.network.forward(state)
return torch.max(actions_value, 1)[1].data.numpy()[0]
def learn(self, state, action, reward, next_state, done):
old_val = self.network.forward(torch.FloatTensor([state])).gather(
1, torch.LongTensor([[action]]))[0]
target_val = self.network.forward(torch.FloatTensor([state]))
if done:
done = 0
target = reward
else:
done = 1
target = reward + GAMMA * torch.max(target_val)
error = abs(old_val[0] - target)
self.memory.add(error.data, (state, action, reward, next_state, done))
if self.memory.tree.n_entries < MEMORY_THRESHOLD:
return
if self.learning_count % UPDATE_TIME == 0:
self.target_network.load_state_dict(self.network.state_dict())
self.learning_count += 1
batch, idxs, is_weights = self.memory.sample(BATCH_SIZE)
state = torch.FloatTensor([x[0] for x in batch])
action = torch.LongTensor([[x[1]] for x in batch])
reward = torch.FloatTensor([[x[2]] for x in batch])
next_state = torch.FloatTensor([x[3] for x in batch])
done = torch.FloatTensor([[x[4]] for x in batch])
eval_q = self.network.forward(state).gather(1, action)
next_q = self.target_network(next_state).detach()
target_q = reward + GAMMA * next_q.max(1)[0].view(BATCH_SIZE, 1) * done
errors = torch.abs(eval_q - target_q).data.numpy().flatten()
loss = self.loss_func(eval_q, target_q)
for i in range(BATCH_SIZE):
idx = idxs[i]
self.memory.update(idx, errors[i])
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
def reset(self, seed=0):
# Reset time
self.t = 0
# Set seed value
np.random.seed(seed)
# Reset replay buffer
self.replay_buffer = Memory(self.n_replay)
# Rebuild model
self.build_model(self.env.size[0] * self.env.size[1], 1)
def reset(self,seed):
# Reset time
self.t=0
# Set seed value
np.random.seed(seed)
# Reset replay buffer
self.replay_buffer = Memory(self.n_replay)
# Rebuild model
self.build_model(self.n_features,self.env.nA)
def __init__(self, replay_size, memory_size=10000, prioritized=False):
self.step = 0
self.replay_size = replay_size
self.replay_queue = deque(maxlen=self.replay_size)
self.memory_size = memory_size
self.tau = 1e-2 #MountainCar-v0
self.model = self.create_model()
self.prioritized = prioritized
self.target_model = self.create_model()
self.target_model.set_weights(self.model.get_weights())
if self.prioritized:
self.memory = Memory(capacity=memory_size)
def __init__(self):
self.eval_net, self.target_net = Net(), Net()
self.eval_net.cuda()
self.target_net.cuda()
# create prioritized replay memory using SumTree
self.memory = Memory(Train_Configs.MEMORY_CAPACITY)
self.learn_counter = 0
self.optimizer = optim.Adam(self.eval_net.parameters(), lr=Train_Configs.LR,betas=(0.9, 0.99), eps=1e-08, weight_decay=2e-5)
self.loss = nn.MSELoss(reduce=False, size_average=False)
self.fig, self.ax = plt.subplots()
self.discount_factor = Train_Configs.GAMMA
def __init__(self, action_size, buffer_size, batch_size, seed):
"""Initialize a ReplayBuffer object.
Params
======
action_size (int): dimension of each action
buffer_size (int): maximum size of buffer
batch_size (int): size of each training batch
seed (int): random seed
"""
self.action_size = action_size
self.memory = Memory(capacity=buffer_size, replay_beta = REPLAY_BETA, replay_alpha = REPLAY_ALPHA, replay_beta_increment = REPLAY_BETA_INCREMENT)
self.batch_size = batch_size
self.seed = random.seed(seed)
self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
def __init__(self, model_params, env_params):
self.ep = env_params # Environemtn Parameters
self.mp = model_params # Model Parameters
self.model = self._build_model()
# deque: list-like, optimzied for fast access at either end
# self.memory = deque(maxlen = int(model_params.max_memory))
# Prioritized Memory class implementing Sum Tree
self.memory = Memory(model_params.max_memory)
def __init__(self,
state_size,
action_size,
seed,
buffer_size=int(1e6),
batch_size=64,
gamma=0.99,
tau=1e-3,
update_every=3,
num_mc_steps=5,
num_agents=2):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.BATCH_SIZE = batch_size
self.GAMMA = gamma
self.TAU = tau
self.UPDATE_EVERY = update_every
self.num_mc_steps = num_mc_steps
self.experiences = [
ExperienceQueue(num_mc_steps) for _ in range(num_agents)
]
self.memory = Memory(buffer_size)
self.t_step = 0
self.train_start = batch_size
self.mad4pg_agent = [
D4PG(state_size,
action_size,
seed,
device,
num_atoms=N_ATOMS,
q_min=Vmin,
q_max=Vmax),
D4PG(state_size,
action_size,
seed,
device,
num_atoms=N_ATOMS,
q_min=Vmin,
q_max=Vmax)
]
def __init__(self, state_size, action_size, config):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.config = config
self.state_size = state_size
self.action_size = action_size
nodes = self.config.get("nodes", [128, 64])
self.seed = self.config.get("seed", 0)
lr = self.config.get("lr", 1e-4)
memory_size = self.config.get("memory_size", 100000)
self.batch_size = self.config.get("batch_size", 256)
self.discount = self.config.get("discount", 0.9)
self.tau = self.config.get("tau", 0.001)
self.epsilon = self.config.get("epsilon", 0.1)
self.epsilon_end = self.config.get("epsilon_end", 0.0001)
self.epsilon_decay = self.config.get("epsilon_decay", 0.995)
self.learn_every = self.config.get("learn_every", 4)
self.dqn = self.config.get("dqn", "simple")
self.per = self.config.get("per", False)
np.random.seed(self.seed)
random.seed(self.seed)
torch.manual_seed(self.seed)
# Q-Network
if self.dqn == "dueling":
self.qnetwork_local = Dueling_QNetwork(state_size, action_size,
self.seed).to(device)
self.qnetwork_target = Dueling_QNetwork(state_size, action_size,
self.seed).to(device)
else:
self.qnetwork_local = QNetwork(state_size,
action_size,
self.seed,
nodes=nodes).to(device)
self.qnetwork_target = QNetwork(state_size,
action_size,
self.seed,
nodes=nodes).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=lr)
#self.optimizer = optim.RMSprop(self.qnetwork_local.parameters(), lr= lr)
# Replay memory
if self.per:
self.memory = Memory(memory_size)
else:
self.memory = ReplayBuffer(memory_size, self.batch_size, self.seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
self.scores = []
def __init__(self,
state_size,
action_size,
random_seed,
buffer_size=BUFFER_SIZE,
batch_size=BATCH_SIZE):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
buffer_size (int): maximum size of buffer
batch_size (int): size of each training batch
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
self.buffer_size = buffer_size
self.memory = Memory(
capacity=self.buffer_size) # internal memory using SumTree
self.batch_size = batch_size
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size).to(device)
self.actor_target = Actor(state_size, action_size).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=LR_ACTOR)
# Critic Network (w/ Target Network)
self.critic_local = Critic(state_size, action_size).to(device)
self.critic_target = Critic(state_size, action_size).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OUNoise(action_size, random_seed)
# 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.
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 = QNetwork(state_size, action_size, seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = Memory(BUFFER_SIZE)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def __init__(self, state_size, action_size):
self.render = False
self.load_model = False
# get size of state and action
self.state_size = state_size
self.action_size = action_size
self.discount_factor = 0.99
self.learning_rate = 0.001
self.lr_step_size = 10
self.lr_gamma = 0.9
self.memory_size = 2**15
self.epsilon = 1.0
self.epsilon_min = 0.05
self.explore_step = 1000
self.epsilon_decay = 0.99995
self.batch_size = 64
self.train_start = 10000
# create prioritized replay memory using SumTree
self.memory = Memory(self.memory_size)
# create main model and target model
self.model = DQN(state_size, action_size)
self.model.apply(self.weights_init)
self.target_model = DQN(state_size, action_size)
self.optimizer = optim.Adam(self.model.parameters(),
lr=self.learning_rate)
self.scheduler = StepLR(self.optimizer,
step_size=self.lr_step_size,
gamma=self.lr_gamma)
# initialize target model
self.update_target_model()
if self.load_model:
self.model = torch.load('save_model/per_dqn')
self.model.train()
def __init__(self, state_size, action_size):
# if you want to see Cartpole learning, then change to True
self.render = False
self.load_model = False
# get size of state and action
self.state_size = state_size
self.action_size = action_size
# These are hyper parameters for the DQN
self.discount_factor = 0.99
self.learning_rate = 0.001
self.memory_size = 20000
self.epsilon = 1.0
self.epsilon_min = 0.01
self.explore_step = 5000
self.epsilon_decay = (self.epsilon -
self.epsilon_min) / self.explore_step
self.batch_size = 64
self.train_start = 1000
# create prioritized replay memory using SumTree
self.memory = Memory(self.memory_size)
# create main model and target model
self.model = DQN(state_size, action_size)
self.model.apply(self.weights_init)
self.target_model = DQN(state_size, action_size)
self.optimizer = optim.Adam(self.model.parameters(),
lr=self.learning_rate)
# initialize target model
self.update_target_model()
if self.load_model:
self.model = torch.load('save_model/cartpole_dqn')
def __init__(self,
state_size: int = 37,
action_size: int = 4,
seed: int = 44,
gamma: float = 0.99,
tau: float = 1e-3):
"""
Initialize an Agent object.
:param state_size: dimension of each state
:param action_size: dimension of each action
:param seed: random seed for network initialisation
:param gamma: discount factor
:param tau: lag for soft update of target network parameters
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.gamma = gamma
self.tau = tau
self.max_w = 0
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(self.device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(self.device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=self.LR)
# Prioritised Experience Replay memory
self.memory = Memory(self.BUFFER_SIZE)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def __init__(self,
replay_size,
memory_size=10000,
prioritized=False,
load_models=False,
actor_model_file='',
critic_model_file='',
is_eval=False):
self.state_size = 2
self.action_size = 3
self.step = 0
self.replay_size = replay_size
self.replay_queue = deque(maxlen=self.replay_size)
self.memory_size = memory_size
self.prioritized = prioritized
if self.prioritized:
self.memory = Memory(capacity=memory_size)
# Hyper parameters for learning
self.value_size = 1
self.layer_size = 16
self.discount_factor = 0.99
self.actor_learning_rate = 0.0005
self.critic_learning_rate = 0.005
self.is_eval = is_eval
# Create actor and critic neural networks
self.actor = self.build_actor()
self.critic = self.build_critic()
#self.actor.summary()
if load_models:
if actor_model_file:
self.actor.load_weights(actor_model_file)
if critic_model_file:
self.critic.load_weights(critic_model_file)
def __init__(self,
learn_rate=0.001,
img_size=(84, 84),
num_frames=3,
action_size=6,
replay_size=64,
max_memory=20000,
is_test=False,
num_episodes=10):
self.img_size = tuple(img_size) # downsampling image size
self.num_frames = int(num_frames) # Deterministic frameskip
self.learn_rate = float(learn_rate) # optimizer learning rate
self.action_size = int(action_size) # No. of possible actions in env
self.num_epochs = int(1) # Epoch size used for training
self.tau = float(0.01) #
self.is_test = bool(is_test)
# Memory
self.replay_size = int(
replay_size) # Size of minibatch sample from memory
self.memory = Memory(int(max_memory)) # deque(maxlen=max_memory)
# self.memory = deque(maxlen=max_memory)
self.num_episodes = int(num_episodes)
# Agent
self._construct_q_network()
class Agent():
"""Interacts with and learns from the environment."""
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 = QNetwork(state_size, action_size, seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = Memory(BUFFER_SIZE)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
next_state = torch.from_numpy(next_state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
self.qnetwork_target.eval()
with torch.no_grad():
target_action_values = self.qnetwork_target(next_state)
expected_action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
self.qnetwork_target.train()
old_val = expected_action_values[0][action]
new_val = reward
if not done:
new_val += GAMMA * torch.max(target_action_values)
error = abs(old_val - new_val)
# Save experience in replay memory
self.memory.add(error, (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 self.memory.tree.n_entries > BATCH_SIZE:
experiences = self.memory.sample(BATCH_SIZE)
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()).astype(int)
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
"""
mini_batches, idxs, is_weights = experiences
states = torch.from_numpy(np.vstack([mini_batch[0] for mini_batch in mini_batches])).float().to(device)
actions = torch.from_numpy(np.vstack([mini_batch[1] for mini_batch in mini_batches])).long().to(device)
rewards = torch.from_numpy(np.vstack([mini_batch[2] for mini_batch in mini_batches])).float().to(device)
next_states = torch.from_numpy(np.vstack([mini_batch[3] for mini_batch in mini_batches])).float().to(device)
dones = torch.from_numpy(np.vstack([int(mini_batch[4]) for mini_batch in mini_batches])).float().to(device)
## TODO: compute and minimize the loss
"*** YOUR CODE HERE ***"
Q_source_next = self.qnetwork_local(next_states).detach().max(1)[1].unsqueeze(1)
Q_target = self.qnetwork_target(next_states)
Q_double_target = torch.tensor([Q_target[i][max_index] for i, max_index in enumerate(Q_source_next)]).detach().unsqueeze(1)
Q_observed = rewards + (gamma * Q_double_target * (1 - dones))
Q_expected = self.qnetwork_local(states).gather(1, actions)
errors = torch.abs(Q_expected - Q_observed).data.numpy()
# update priority
for i in range(BATCH_SIZE):
idx = idxs[i]
self.memory.update(idx, errors[i])
loss = (torch.FloatTensor(is_weights) * F.mse_loss(Q_expected, Q_observed)).mean()
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)
class PERAgent(OffPolicyAgent):
# construct agent's model separately, so it can be sized according to problem
def __init__(self, n_replay, env, target_policy, behavior_policy, lr, discount, type = 'BC'):
super().__init__(n_replay, env, target_policy, behavior_policy, lr, discount, type)
# reseed numpy, reset weights of network
# Reset must be performed before every episode
def reset(self,seed):
# Reset time
self.t=0
# Set seed value
np.random.seed(seed)
# Reset replay buffer
self.replay_buffer = Memory(self.n_replay)
# Rebuild model
self.build_model(self.n_features,self.env.nA)
def generate_action(self, s, target_policy_sel = True):
pval = self.target_policy[s] if target_policy_sel else self.behavior_policy[s]
return np.random.choice(a=self.actions, p=pval)
def generate_all_actions(self,target_policy_sel = True):
return np.array([self.generate_action(item, target_policy_sel) for item in range(self.target_policy.shape[0])])
# Generate steps of experience
def generate_experience(self, k=16):
# Initialize environment
s = self.env.reset()
done = False
steps = 0
# For each step
while steps < k:
# choose action according to behavior policy
a = self.generate_action(s,False)
# Take a step in environment based on chosen action
(s2,r,done,_) = self.env.step(a)
# Compute importance ratios
ratio = self.target_policy[s,a] / self.behavior_policy[s,a]
# states and target action for Computing TD Error
current_state = self.construct_features([s])
next_state = self.construct_features([s2])
target_policy_action = self.generate_action(s,True)
# Get bootstrap estimate of next state action values
value_s = self.model.predict([current_state,np.zeros(current_state.shape[0])])
value_next_s = self.model.predict([next_state,np.zeros(next_state.shape[0])])
updated_val = r if done else (r + self.discount*value_next_s[0][target_policy_action])
# Compute TD error
td_error = np.abs(updated_val - value_s[0][a])
# Stop execution if weights blow up - not converged
if td_error > 10**5:
return 1
# Add experience to IR replay buffer
self.replay_buffer.add_per(td_error, (s,a,r,s2))
# Set for next step
s=s2
self.t += 1
steps += 1
# If episode ends, reset environment
if done:
done = False
s = self.env.reset()
return 0
# do batch of training using replay buffer
def train_batch(self, n_samples, batch_size):
# Sample a minibatch from replay buffer
data_samples, idxs, ratios, buffer_total = self.replay_buffer.sample(n_samples)
# Extract rewards, states, next states, actions from samples
rewards = extract_transition_components(data_samples, TransitionComponent.reward)
next_states = extract_transition_components(data_samples, TransitionComponent.next_state)
next_state_features = self.construct_features(next_states)
states = extract_transition_components(data_samples, TransitionComponent.state)
state_features = self.construct_features(states)
actions = extract_transition_components(data_samples, TransitionComponent.action)
# Calculate Target policy actions
target_policy_actions = np.array([self.generate_action(state, True) for state in states])
# Calculate state values for TD error
next_values_sa = self.model.predict([next_state_features, np.zeros(next_state_features.shape[0])])
next_values = np.choose(target_policy_actions,next_values_sa.T)
# v(s') is zero for terminal state, so need to fix model prediction
for i in range(n_samples):
# if experience ends in terminal state, value function returns 0
if next_states[i] == -1 or next_states[i] == 10: #TODO this only works for randomwalk of size 10
next_values[i] = 0.0
# Compute targets by bootstrap estimates
targets = (rewards + self.discount*next_values)
# Compute error for updating priorities
pred_values = self.model.predict([state_features, np.zeros(state_features.shape[0])])
final_targets = np.copy(pred_values)
np.put_along_axis(final_targets, np.expand_dims(actions,axis = 1),targets[:,np.newaxis],axis = 1)
pred = np.choose(actions, pred_values.T)
error = np.abs(pred - targets)
# Priority update
for i in range(batch_size):
self.replay_buffer.update(idxs[i], error[i])
# train on samples
self.model.fit([state_features, ratios], final_targets, batch_size=batch_size, verbose=0)
class Dqn():
def __init__(self):
self.eval_net, self.target_net = Net(), Net()
self.eval_net.cuda()
self.target_net.cuda()
# create prioritized replay memory using SumTree
self.memory = Memory(Train_Configs.MEMORY_CAPACITY)
self.learn_counter = 0
self.optimizer = optim.Adam(self.eval_net.parameters(), lr=Train_Configs.LR,betas=(0.9, 0.99), eps=1e-08, weight_decay=2e-5)
self.loss = nn.MSELoss(reduce=False, size_average=False)
self.fig, self.ax = plt.subplots()
self.discount_factor = Train_Configs.GAMMA
def store_trans(self, state_path, action, reward, next_state_path,done):
## action type: id
x, y, c = my_utils.translate_actionID_to_XY_and_channel(action)
trans = state_path+'#'+str(action)+'#'+str(reward)+'#'+next_state_path#np.hstack((state, [action], [reward], next_state))
#------ calculate TD errors from (s,a,r,s'), #--only from the first depth image, without considering other 9 rotated depth images
state_d = state_path
next_state_d = next_state_path
if c > 0:
state_d = my_utils.get_rotate_depth(c,state_d)
next_state_d = my_utils.get_rotate_depth(c, next_state_d)
state_depth = my_utils.copy_depth_to_3_channel(state_d).reshape(1, 3, DIM_STATES[0], DIM_STATES[1])
next_state_depth = my_utils.copy_depth_to_3_channel(next_state_d).reshape(1, 3, DIM_STATES[0], DIM_STATES[1])
if c == 0:
state_rgb = my_utils.trans_HWC_to_CHW(cv2.imread(state_path.replace('npy','png').replace('state_depth','state_image'))).reshape(1, 3, DIM_STATES[0], DIM_STATES[1])
next_state_rgb = my_utils.trans_HWC_to_CHW(cv2.imread(next_state_path.replace('npy','png').replace('state_depth', 'state_image'))).reshape(1, 3, DIM_STATES[0], DIM_STATES[1])
else:
state_rgb = my_utils.get_rotate_rgb(c,state_path.replace('npy','png').replace('state_depth','state_image')).reshape(1, 3, DIM_STATES[0], DIM_STATES[1])
next_state_rgb = my_utils.get_rotate_rgb(c,next_state_path.replace('npy','png').replace('state_depth','state_image')).reshape(1, 3, DIM_STATES[0], DIM_STATES[1])
# # normlize
# state_depth = (state_depth - Train_Configs.MIN_DEPTH_ARR) / (Train_Configs.MAX_DEPTH_ARR - Train_Configs.MIN_DEPTH_ARR)
# next_state_depth = (next_state_depth - Train_Configs.MIN_DEPTH_ARR) / (Train_Configs.MAX_DEPTH_ARR - Train_Configs.MIN_DEPTH_ARR)
# numpy to tensor
state_depth = torch.cuda.FloatTensor(state_depth)
next_state_depth = torch.cuda.FloatTensor(next_state_depth)
state_rgb = torch.cuda.FloatTensor(state_rgb)
next_state_rgb = torch.cuda.FloatTensor(next_state_rgb)
target_singleChannel_q_map = self.eval_net.forward(state_rgb,state_depth)#dim:[1,1,224,224],CHANNEL=1
# x,y,c = my_utils.translate_actionID_to_XY_and_channel(action)
old_val = target_singleChannel_q_map[0][0][x][y]
# old_val = target[0][action]
target_val_singleChannel_q_map = self.target_net.forward(next_state_rgb,next_state_depth)#dim:[1,1,224,224]
if done == 1:
target_q = reward # target[0][action] = reward
else:
target_q = reward + self.discount_factor * torch.max(target_val_singleChannel_q_map) # target[0][action] = reward + self.discount_factor * torch.max(target_val)
error = abs(old_val - target_q)
self.memory.add(float(error), trans)
def choose_action(self, state_path,EPSILON):
state_rgb = []
state_depth = []
state_rgb.append(my_utils.trans_HWC_to_CHW(cv2.imread(state_path.replace('npy','png').replace('state_depth','state_image'))))
state_depth.append(my_utils.copy_depth_to_3_channel(state_path))#dim:[3, DIM_STATES[0], DIM_STATES[1]]#.reshape(1, 3, DIM_STATES[0], DIM_STATES[1]))
for i in range(1,Train_Configs.ROTATION_BINS):
state_rotate_rgb = my_utils.get_rotate_rgb(i,state_path.replace('npy','png').replace('state_depth','state_image'))
state_rgb.append(state_rotate_rgb)
#------------------------
state_rotate_depth = my_utils.get_rotate_depth(i,state_path)
state_rotate_3_depth = my_utils.copy_depth_to_3_channel(state_rotate_depth)
state_depth.append(state_rotate_3_depth)
state_rgb = np.array(state_rgb)
state_depth = np.array(state_depth)
# # normlize
# state_depth = (state_depth - Train_Configs.MIN_DEPTH_ARR) / (Train_Configs.MAX_DEPTH_ARR - Train_Configs.MIN_DEPTH_ARR)
# numpy to tensor
state_rgb = torch.cuda.FloatTensor(state_rgb) # dim:[INPUT_IMAGE,3,224,224]
state_depth = torch.cuda.FloatTensor(state_depth) #dim:[INPUT_IMAGE,3,224,224]
# random exploration
prob = np.min((EPSILON,1))
p_select = np.array([prob, 1 - prob])
selected_ac_type = np.random.choice([0, 1], p=p_select.ravel())
if selected_ac_type == 0:#origin predicted action
target_multiChannel_q_map = self.eval_net.forward(state_rgb,state_depth) # dim:[INPUT_IMAGES,1,224,224]
action = my_utils.find_maxQ_in_qmap(target_multiChannel_q_map.cpu().detach().numpy())
ac_ty = '0'
else:
if np.random.randn() <= 0.5:#sample action according to depth image
action = my_utils.select_randpID_from_mask(state_path)
ac_ty = '1'
else:# random sample
action = np.random.randint(0,DIM_ACTIONS)
ac_ty = '2'
return ac_ty,action # the id of action
def plot(self, ax, x):
ax.cla()
ax.set_xlabel("episode")
ax.set_ylabel("total reward")
ax.plot(x, 'b-')
plt.pause(0.000000000000001)
def load_batch_data(self,batch_list):#batch_list.dim:[batch_size]
# print(batch_list)
batch_state_rgb = []
batch_state_depth = []
batch_action = []
batch_reward = []
batch_next_state_rgb = []
batch_next_state_depth = []
for item in batch_list:
data = item.split('#')#state+'#'+str(action)+'#'+str(reward)+'#'+next_state
action_id = int(data[1])
batch_state_rgb.append(my_utils.get_rotate_rgb(action_id,data[0].replace('npy','png').replace('state_depth','state_image')))
batch_state_depth.append(my_utils.copy_depth_to_3_channel(my_utils.get_rotate_depth(action_id,data[0])).reshape((3,DIM_STATES[0],DIM_STATES[1])))
batch_action.append([int(data[1])])
batch_reward.append([float(data[2])])
batch_next_state_rgb.append(my_utils.get_rotate_rgb(action_id, data[3].replace('npy','png').replace('state_depth', 'state_image')))
batch_next_state_depth.append(my_utils.copy_depth_to_3_channel(my_utils.get_rotate_depth(action_id,data[3])).reshape((3,DIM_STATES[0],DIM_STATES[1])))
batch_state_depth = np.array(batch_state_depth)
batch_next_state_depth = np.array(batch_next_state_depth)
# # normlize
# batch_state_depth = (batch_state_depth - Train_Configs.MIN_DEPTH_ARR) / (Train_Configs.MAX_DEPTH_ARR - Train_Configs.MIN_DEPTH_ARR)
# batch_next_state_depth = (batch_next_state_depth - Train_Configs.MIN_DEPTH_ARR) / (Train_Configs.MAX_DEPTH_ARR - Train_Configs.MIN_DEPTH_ARR)
return torch.cuda.FloatTensor(batch_state_rgb),torch.cuda.FloatTensor(batch_state_depth),torch.cuda.LongTensor(batch_action),torch.cuda.FloatTensor(batch_reward),torch.cuda.FloatTensor(batch_next_state_rgb),torch.cuda.FloatTensor(batch_next_state_depth)
def learn(self):
# learn 100 times then the target network update
if self.learn_counter % Train_Configs.Q_NETWORK_ITERATION ==0:
self.target_net.load_state_dict(self.eval_net.state_dict())
self.learn_counter+=1
mini_batch, idxs, is_weights = self.memory.sample(Train_Configs.BATCH_SIZE)#
batch_state_rgb,batch_state_depth,batch_action,batch_reward,batch_next_state_rgb,batch_next_state_depth = self.load_batch_data(mini_batch)#dim:[1]
eval_singleChannel_q_map = self.eval_net(batch_state_rgb,batch_state_depth) # dim:[BATCH_SIZE,1,224,224]
x_y_c_list = my_utils.translate_actionID_to_XY_and_channel_batch(batch_action)
# old_val = target_multiChannel_q_map[0][c][x][y]
batch_q = []
# for xyc in x_y_c_list:
for i in range(len(x_y_c_list)):
xyc = x_y_c_list[i]
batch_q.append([eval_singleChannel_q_map[i][0][xyc[0]][xyc[1]]])
q_eval = torch.cuda.FloatTensor(batch_q)#self.eval_net(batch_state).gather(1, batch_action)#action: a value in range [0,DIM_ACTIONS-1]
q_eval = Variable(q_eval.cuda(), requires_grad=True)
target_singleChannel_q_map = self.target_net(batch_next_state_rgb,batch_next_state_depth).cpu().detach().numpy()#q_next,dim:[BATCH_SIZE,1,224,224]
batch_q_next = []
for b_item in target_singleChannel_q_map:#dim:[1,224,224]
batch_q_next.append([np.max(b_item)])
q_next = torch.cuda.FloatTensor(batch_q_next)
# q_next = Variable(q_next.cuda(), requires_grad=True)
q_target = batch_reward + Train_Configs.GAMMA*q_next
q_target = Variable(q_target.cuda(), requires_grad=True)
# self.average_q = q_eval.mean()
weight_tensor = torch.cuda.FloatTensor(is_weights)#
weight_tensor = weight_tensor.reshape((Train_Configs.BATCH_SIZE,1))
weight_tensor = Variable(weight_tensor.cuda(), requires_grad=False)
loss = (weight_tensor * self.loss(q_eval, q_target)).mean()##(torch.FloatTensor(is_weights) * F.mse_loss(pred, target)).mean()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return float(loss),float(q_eval.mean())
class MAD4PG:
"""Interacts with and learns from the environment."""
def __init__(self,
state_size,
action_size,
seed,
buffer_size=int(1e6),
batch_size=64,
gamma=0.99,
tau=1e-3,
update_every=3,
num_mc_steps=5,
num_agents=2):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.BATCH_SIZE = batch_size
self.GAMMA = gamma
self.TAU = tau
self.UPDATE_EVERY = update_every
self.num_mc_steps = num_mc_steps
self.experiences = [
ExperienceQueue(num_mc_steps) for _ in range(num_agents)
]
self.memory = Memory(buffer_size)
self.t_step = 0
self.train_start = batch_size
self.mad4pg_agent = [
D4PG(state_size,
action_size,
seed,
device,
num_atoms=N_ATOMS,
q_min=Vmin,
q_max=Vmax),
D4PG(state_size,
action_size,
seed,
device,
num_atoms=N_ATOMS,
q_min=Vmin,
q_max=Vmax)
]
def acts(self, states, add_noise=0.0):
acts = []
for s, a in zip(states, self.mad4pg_agent):
acts.append(a.act(np.expand_dims(s, 0), add_noise))
return np.vstack(acts)
# borrow from https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On/tree/master/Chapter14
def distr_projection(self, next_distr_v, rewards_v, dones_mask_t, gamma):
next_distr = next_distr_v.data.cpu().numpy()
rewards = rewards_v.data.cpu().numpy()
dones_mask = dones_mask_t.cpu().numpy().astype(np.bool)
batch_size = len(rewards)
proj_distr = np.zeros((batch_size, N_ATOMS), dtype=np.float32)
dones_mask = np.squeeze(dones_mask)
rewards = rewards.reshape(-1)
for atom in range(N_ATOMS):
tz_j = np.minimum(
Vmax,
np.maximum(Vmin, rewards + (Vmin + atom * DELTA_Z) * gamma))
b_j = (tz_j - Vmin) / DELTA_Z
l = np.floor(b_j).astype(np.int64)
u = np.ceil(b_j).astype(np.int64)
eq_mask = u == l
proj_distr[eq_mask, l[eq_mask]] += next_distr[eq_mask, atom]
ne_mask = u != l
proj_distr[ne_mask,
l[ne_mask]] += next_distr[ne_mask,
atom] * (u - b_j)[ne_mask]
proj_distr[ne_mask,
u[ne_mask]] += next_distr[ne_mask,
atom] * (b_j - l)[ne_mask]
if dones_mask.any():
proj_distr[dones_mask] = 0.0
tz_j = np.minimum(Vmax, np.maximum(Vmin, rewards[dones_mask]))
b_j = (tz_j - Vmin) / DELTA_Z
l = np.floor(b_j).astype(np.int64)
u = np.ceil(b_j).astype(np.int64)
eq_mask = u == l
if dones_mask.shape == ():
if dones_mask:
proj_distr[0, l] = 1.0
else:
ne_mask = u != l
proj_distr[0, l] = (u - b_j)[ne_mask]
proj_distr[0, u] = (b_j - l)[ne_mask]
else:
eq_dones = dones_mask.copy()
eq_dones[dones_mask] = eq_mask
if eq_dones.any():
proj_distr[eq_dones, l[eq_mask]] = 1.0
ne_mask = u != l
ne_dones = dones_mask.copy()
ne_dones[dones_mask] = ne_mask
if ne_dones.any():
proj_distr[ne_dones, l[ne_mask]] = (u - b_j)[ne_mask]
proj_distr[ne_dones, u[ne_mask]] = (b_j - l)[ne_mask]
return torch.FloatTensor(proj_distr).to(device)
def step(self, states, actions, rewards, next_states, dones):
for agent_index in range(len(self.mad4pg_agent)):
agent_experiences = self.experiences[agent_index]
agent_experiences.states.appendleft(states[agent_index])
agent_experiences.rewards.appendleft(rewards[agent_index] *
self.GAMMA**self.num_mc_steps)
agent_experiences.actions.appendleft(actions[agent_index])
if len(agent_experiences.rewards) == self.num_mc_steps or dones[
agent_index]: # N-steps return: r= r1+gamma*r2+..+gamma^(t-1)*rt
done_tensor = torch.tensor(
dones[agent_index]).float().to(device)
condition = True
while condition:
for i in range(len(agent_experiences.rewards)):
agent_experiences.rewards[i] /= self.GAMMA
state = torch.tensor(
agent_experiences.states[-1]).float().unsqueeze(0).to(
device)
next_state = torch.tensor(
next_states[agent_index]).float().unsqueeze(0).to(
device)
action = torch.tensor(
agent_experiences.actions[-1]).float().unsqueeze(0).to(
device)
sum_reward = torch.tensor(sum(
agent_experiences.rewards)).float().unsqueeze(0).to(
device)
with evaluating(
self.mad4pg_agent[agent_index]) as cur_agent:
q_logits_expected = cur_agent.critic_local(
state, action)
action_next = cur_agent.actor_target(next_state)
q_target_logits_next = cur_agent.critic_target(
next_state, action_next)
q_target_distr_next = F.softmax(q_target_logits_next,
dim=1)
q_target_distr_next_projected = self.distr_projection(
q_target_distr_next, sum_reward, done_tensor,
self.GAMMA**self.num_mc_steps)
cross_entropy = -F.log_softmax(
q_logits_expected,
dim=1) * q_target_distr_next_projected
error = cross_entropy.sum(dim=1).mean().cpu().data
self.memory.add(
error,
(states[agent_index], actions[agent_index], sum_reward,
next_states[agent_index], dones[agent_index]))
agent_experiences.states.pop()
agent_experiences.rewards.pop()
agent_experiences.actions.pop()
condition = False and dones[agent_index] and len(
agent_experiences.states) > 0
if dones[agent_index]:
agent_experiences.states.clear()
agent_experiences.rewards.clear()
agent_experiences.actions.clear()
self.t_step = (self.t_step + 1) % self.UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
# print(self.memory.tree.n_entries)
if self.memory.tree.n_entries > self.train_start:
for agent_index in range(len(self.mad4pg_agent)):
sampled_experiences, idxs = self.sample()
self.learn(self.mad4pg_agent[agent_index],
sampled_experiences, idxs)
def sample(self):
# prioritized experience replay
mini_batch, idxs, is_weights = self.memory.sample(self.BATCH_SIZE)
mini_batch = np.array(mini_batch).transpose()
statess = np.vstack([m for m in mini_batch[0] if m is not None])
actionss = np.vstack([m for m in mini_batch[1] if m is not None])
rewardss = np.vstack([m for m in mini_batch[2] if m is not None])
next_statess = np.vstack([m for m in mini_batch[3] if m is not None])
doness = np.vstack([m for m in mini_batch[4] if m is not None])
# bool to binary
doness = doness.astype(int)
statess = torch.from_numpy(statess).float().to(device)
actionss = torch.from_numpy(actionss).float().to(device)
rewardss = torch.from_numpy(rewardss).float().to(device)
next_statess = torch.from_numpy(next_statess).float().to(device)
doness = torch.from_numpy(doness).float().to(device)
return (statess, actionss, rewardss, next_statess, doness), idxs
def learn(self, agent, experiences, idxs):
"""Update policy and value parameters using given batch of experience tuples.
Q_targets = r + γ * critic_target(next_state, actor_target(next_state))
where:
actor_target(state) -> action
critic_target(state, action) -> Q-value
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# ---------------------------- update critic ---------------------------- #
# Get predicted next-state actions and Q values from target models
# Compute critic loss
q_logits_expected = agent.critic_local(states, actions)
actions_next = agent.actor_target(next_states)
q_targets_logits_next = agent.critic_target(next_states, actions_next)
q_targets_distr_next = F.softmax(q_targets_logits_next, dim=1)
q_targets_distr_projected_next = self.distr_projection(
q_targets_distr_next, rewards, dones,
self.GAMMA**self.num_mc_steps)
cross_entropy = -F.log_softmax(q_logits_expected,
dim=1) * q_targets_distr_projected_next
critic_loss = cross_entropy.sum(dim=1).mean()
with torch.no_grad():
errors = cross_entropy.sum(dim=1).cpu().data.numpy()
# update priority
for i in range(self.BATCH_SIZE):
idx = idxs[i]
self.memory.update(idx, errors[i])
# Minimize the loss
agent.critic_optimizer.zero_grad()
critic_loss.backward()
agent.critic_optimizer.step()
# Compute actor loss
actions_pred = agent.actor_local(states)
crt_distr_v = agent.critic_local(states, actions_pred)
actor_loss = -agent.critic_local.distr_to_q(crt_distr_v)
actor_loss = actor_loss.mean()
# Minimize the loss
agent.actor_optimizer.zero_grad()
actor_loss.backward()
agent.actor_optimizer.step()
# ----------------------- update target networks ----------------------- #
agent.soft_update(agent.critic_local, agent.critic_target, self.TAU)
agent.soft_update(agent.actor_local, agent.actor_target, self.TAU)
discount_factor = 0.5
mini_batch_size = 10
copy_target_net = 6
train_iter = 30
save_freq = 5
save_root = "models_with_novel_422_batch_{}".format(mini_batch_size)
primitive_lr = 2.5e-4
dexnet_lr = 5e-5
suction_1_sampled = np.zeros(memory_capacity[0])
suction_2_sampled = np.zeros(memory_capacity[1])
gripper_sampled = np.zeros(memory_capacity[2])
#model_str = "../training/logger_010/models/10_121.pth"
model_str = "evaluate_model/600.pth"
suction_1_memory = Memory(memory_capacity[0])
suction_2_memory = Memory(memory_capacity[1])
gripper_memory = Memory(memory_capacity[2])
suction_1_memory.load_memory("suction_1_mixed_400.pkl")
suction_2_memory.load_memory("suction_2_mixed_200.pkl")
gripper_memory.load_memory("gripper_mixed_200.pkl")
#suction_1_memory.tree.reset_priority()
#suction_2_memory.tree.reset_priority()
#gripper_memory.tree.reset_priority()
compare_color = "../training/logger_009/images/color_000005.jpg"
compare_depth = "../training/logger_009/depth_data/depth_data_000005.npy"
def create_path():
cwd = os.getcwd() + "/" + save_root
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self,
state_size,
action_size,
random_seed,
buffer_size=BUFFER_SIZE,
batch_size=BATCH_SIZE):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
buffer_size (int): maximum size of buffer
batch_size (int): size of each training batch
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
self.buffer_size = buffer_size
self.memory = Memory(
capacity=self.buffer_size) # internal memory using SumTree
self.batch_size = batch_size
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size).to(device)
self.actor_target = Actor(state_size, action_size).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=LR_ACTOR)
# Critic Network (w/ Target Network)
self.critic_local = Critic(state_size, action_size).to(device)
self.critic_target = Critic(state_size, action_size).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OUNoise(action_size, random_seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self,
state,
action,
reward,
next_state,
done,
batch_size=BATCH_SIZE,
update_every=UPDATE_EVERY):
"""Save experience in replay memory, and use random sample from buffer to learn."""
# Save experience / reward
self.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:
# Learn, if enough samples are available in memory
if self.memory.tree.n_entries >= batch_size:
experiences, idxs, is_weights = self.sample()
self.learn(experiences, idxs, is_weights)
def act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
state = torch.from_numpy(state).float().to(device)
self.actor_local.eval()
with torch.no_grad():
action = self.actor_local(state).cpu().data.numpy()
self.actor_local.train()
if add_noise:
#action = [act + self.noise.sample() for act in action]
action += self.noise.sample()
return np.clip(action, -1, 1)
def reset(self):
self.noise.reset()
def learn(self,
experiences,
idxs,
is_weights,
batch_size=BATCH_SIZE,
gamma=GAMMA):
"""Update policy and value parameters using given batch of experience tuples.
Q_targets = r + γ * critic_target(next_state, actor_target(next_state))
where:
actor_target(state) -> action
critic_target(state, action) -> Q-value
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# ---------------------------- update critic ---------------------------- #
# Get predicted next-state actions and Q values from target models
actions_next = self.actor_target(next_states)
Q_targets_next = self.critic_target(next_states, actions_next)
# Compute Q targets for current states (y_i)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Compute critic loss
Q_expected = self.critic_local(states, actions)
#Loss calculation
critic_loss = (torch.from_numpy(is_weights).float().to(device) *
F.mse_loss(Q_expected, Q_targets)).mean()
# Minimize the loss
self.critic_optimizer.zero_grad()
critic_loss.backward()
#Introducing gradient clipping
torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1)
self.critic_optimizer.step()
# ---------------------------- update actor ---------------------------- #
# Compute actor loss
actions_pred = self.actor_local(states)
actor_loss = -self.critic_local(states, actions_pred).mean()
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# ----------------------- update target networks ----------------------- #
self.soft_update(self.critic_local, self.critic_target, TAU)
self.soft_update(self.actor_local, self.actor_target, TAU)
#.......................update priorities in prioritized replay buffer.......#
#Calculate errors used in prioritized replay buffer
errors = (Q_expected - Q_targets).squeeze().cpu().data.numpy()
# update priority
for i in range(batch_size):
idx = idxs[i]
self.memory.update(idx, errors[i])
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)
def add(self, state, action, reward, next_state, done, gamma=GAMMA):
"""Add a new experience to memory."""
next_state_torch = torch.from_numpy(next_state).float().to(device)
reward_torch = torch.unsqueeze(
torch.from_numpy(np.array(reward)).float().to(device), 1)
done_torch = torch.unsqueeze(
torch.from_numpy(np.array(done).astype(
np.uint8)).float().to(device), 1)
state_torch = torch.from_numpy(state).float().to(device)
action_torch = torch.from_numpy(action).float().to(device)
self.actor_target.eval()
self.critic_target.eval()
self.critic_local.eval()
with torch.no_grad():
action_next = self.actor_target(next_state_torch)
Q_target_next = self.critic_target(next_state_torch, action_next)
Q_target = reward_torch + (gamma * Q_target_next *
(1 - done_torch))
Q_expected = self.critic_local(state_torch, action_torch)
self.actor_local.train()
self.critic_target.train()
self.critic_local.train()
#Error used in prioritized replay buffer
error = (Q_expected - Q_target).squeeze().cpu().data.numpy()
#Adding experiences to prioritized replay buffer
for i in np.arange(len(reward)):
self.memory.add(
error[i],
(state[i], action[i], reward[i], next_state[i], done[i]))
def sample(self):
"""Randomly sample a batch of experiences from memory."""
experiences, idxs, is_weights = self.memory.sample(self.batch_size)
states = np.vstack([e[0] for e in experiences])
states = torch.from_numpy(states).float().to(device)
actions = np.vstack([e[1] for e in experiences])
actions = torch.from_numpy(actions).float().to(device)
rewards = np.vstack([e[2] for e in experiences])
rewards = torch.from_numpy(rewards).float().to(device)
next_states = np.vstack([e[3] for e in experiences])
next_states = torch.from_numpy(next_states).float().to(device)
dones = np.vstack([e[4] for e in experiences]).astype(np.uint8)
dones = torch.from_numpy(dones).float().to(device)
return (states, actions, rewards, next_states, dones), idxs, is_weights
lr = 0.00001
C = 10000
clip_norm = 1
replay_buffer_size = 100000
DOUBLE_DQN = True
load_weights = False
average_proportion = 0.01
OPT = Adam
Hindsight_experience_replay = True
# Variables
env = GoalEnvironment(random_GOAL = True)
#env = gym.make('Acrobot-v1')
#env = GridGoalEnvironment(n = 10,shuffle_goal = True, discrete_goal = True, stochastic = True, holonomic = False)
replay_buffer = Memory(replay_buffer_size)
#replay_buffer = list()
timestep = 0
episode_timestep_history = list()
reward_history = list()
C = C *update_frequency
exploration_linear_decay = 1/exploration_annealing_frames
running_reward = 0
scaler = StandardScaler()
min_reward = - 1/(1-gamma)
temporal_diff_mean = 0
update_count = 0
# Model definition and clonning
Q_net = Sequential()
Q_net.add(Dense(64, input_shape = env.observation_space.shape, activation = 'relu'))
class DQNAgent():
def __init__(self, state_size, action_size):
self.render = False
self.load_model = False
# get size of state and action
self.state_size = state_size
self.action_size = action_size
self.discount_factor = 0.99
self.learning_rate = 0.001
self.lr_step_size = 10
self.lr_gamma = 0.9
self.memory_size = 2**15
self.epsilon = 1.0
self.epsilon_min = 0.05
self.explore_step = 1000
self.epsilon_decay = 0.99995
self.batch_size = 64
self.train_start = 10000
# create prioritized replay memory using SumTree
self.memory = Memory(self.memory_size)
# create main model and target model
self.model = DQN(state_size, action_size)
self.model.apply(self.weights_init)
self.target_model = DQN(state_size, action_size)
self.optimizer = optim.Adam(self.model.parameters(),
lr=self.learning_rate)
self.scheduler = StepLR(self.optimizer,
step_size=self.lr_step_size,
gamma=self.lr_gamma)
# initialize target model
self.update_target_model()
if self.load_model:
self.model = torch.load('save_model/per_dqn')
self.model.train()
# weight xavier initialize
def weights_init(self, m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
torch.nn.init.xavier_uniform_(m.weight)
# after some time interval update the target model to be same with model
def update_target_model(self):
self.target_model.load_state_dict(self.model.state_dict())
# get action from model using epsilon-greedy policy
def get_action(self, state):
if np.random.rand() <= self.epsilon:
return random.randrange(self.action_size)
else:
state = torch.from_numpy(state).float()
q_value = self.model(state)
_, action = torch.max(q_value, 1)
return int(action)
# save sample (error,<s,a,r,s'>) to the replay memory
def append_sample(self, state, action, reward, next_state, done):
target = self.model(torch.tensor(state).float()).data
old_val = target[0][action]
target_val = self.target_model(torch.tensor(next_state).float()).data
if done:
target[0][action] = reward
else:
target[0][action] = reward + \
self.discount_factor * torch.max(target_val)
error = abs(old_val - target[0][action])
self.memory.add(error, (state, action, reward, next_state, done))
# pick samples from prioritized replay memory (with batch_size)
def train_model(self):
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
self.epsilon = max(self.epsilon, self.epsilon_min)
mini_batch, idxs, is_weights = self.memory.sample(self.batch_size)
mini_batch = np.array(mini_batch).transpose()
states = np.vstack(mini_batch[0])
actions = list(mini_batch[1])
rewards = list(mini_batch[2])
next_states = np.vstack(mini_batch[3])
dones = mini_batch[4]
# bool to binary
dones = dones.astype(int)
# Q function of current state
states = torch.tensor(states).float()
pred = self.model(states)
# one-hot encoding
a = torch.tensor(actions, dtype=torch.long).view(-1, 1)
one_hot_action = torch.zeros(self.batch_size, self.action_size)
one_hot_action.scatter_(1, a, 1)
pred = torch.sum(pred.mul(one_hot_action), dim=1)
# Q function of next state
next_states = torch.tensor(next_states, dtype=torch.float)
next_pred = self.target_model(next_states.float()).data
rewards = torch.tensor(rewards, dtype=torch.float)
dones = torch.tensor(dones, dtype=torch.float)
# Q Learning: get maximum Q value at s' from target model
target = rewards + (1 - dones) * \
self.discount_factor * next_pred.max(1)[0]
errors = torch.abs(pred - target).data.numpy()
# update priority
for i in range(self.batch_size):
idx = idxs[i]
self.memory.update(idx, errors[i])
self.optimizer.zero_grad()
# MSE Loss function
loss = (torch.tensor(is_weights).float() *
F.mse_loss(pred, target)).mean()
loss.backward()
# and train
self.optimizer.step()
return loss.item()
def __init__(self,
state_size,
action_size,
seed,
BUFFER_SIZE=int(1e5),
BATCH_SIZE=64,
GAMMA=0.99,
TAU=1e-3,
LR_ACTOR=1e-4,
LR_CRITIC=3e-4,
WEIGHT_DECAY=0.0001,
UPDATE_EVERY=4,
IsPR=False,
N_step=1,
IsD4PG_Cat=False):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.BUFFER_SIZE = BUFFER_SIZE
self.BATCH_SIZE = BATCH_SIZE
self.GAMMA = GAMMA
self.TAU = TAU
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.UPDATE_EVERY = UPDATE_EVERY
self.N_step = N_step
self.IsD4PG_Cat = IsD4PG_Cat
self.rewards_queue = deque(maxlen=N_step)
self.states_queue = deque(maxlen=N_step)
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, seed).to(device)
self.actor_target = Actor(state_size, action_size, seed).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=LR_ACTOR)
# Critic Network (w/ Target Network)
if IsD4PG_Cat:
self.critic_local = CriticD4PG(state_size,
action_size,
seed,
n_atoms=N_ATOMS,
v_min=Vmin,
v_max=Vmax).to(device)
self.critic_target = CriticD4PG(state_size,
action_size,
seed,
n_atoms=N_ATOMS,
v_min=Vmin,
v_max=Vmax).to(device)
else:
self.critic_local = Critic(state_size, action_size,
seed).to(device)
self.critic_target = Critic(state_size, action_size,
seed).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Replay memory
self.BATCH_SIZE = BATCH_SIZE
self.IsPR = IsPR
if IsPR:
self.memory = Memory(BUFFER_SIZE) # prioritized experienc replay
else:
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
self.seed)
# Noise process
self.noise = OUNoise(action_size, self.seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
self.train_start = 2000
from prioritized_memory import Memory
import numpy as np
import cv2
from utils import Transition
R = 5.0
m1 = Memory(1000)
m2 = Memory(1000)
m3 = Memory(1000)
M1 = Memory(1500)
M2 = Memory(1500)
M3 = Memory(1500)
m1.load_memory("../training/logger_013/suction_1_memory.pkl")
m2.load_memory("../training/logger_013/suction_2_memory.pkl")
m3.load_memory("../training/logger_013/gripper_memory.pkl")
empty_color = []
empty_depth = []
for i in range(m1.length):
M1.add(m1.tree.data[i])
M2.add(m2.tree.data[i])
M3.add(m3.tree.data[i])
for i in range(m1.length):
# Invalid point is common
if m1.tree.data[i].reward == -3 * R:
transition = m1.tree.data[i]
pixel_index = transition.pixel_idx
