def __init__(self,
meta_controller_experience_memory=None,
lr=0.00025,
alpha=0.95,
eps=0.01,
batch_size=32,
gamma=0.99,
num_options=12):
# expereince replay memory
self.meta_controller_experience_memory = meta_controller_experience_memory
self.lr = lr # learning rate
self.alpha = alpha # optimizer parameter
self.eps = 0.01 # optimizer parameter
self.gamma = 0.99
# BUILD MODEL
USE_CUDA = torch.cuda.is_available()
if torch.cuda.is_available() and torch.cuda.device_count() > 1:
self.device = torch.device("cuda:1")
elif torch.cuda.device_count() == 1:
self.device = torch.device("cuda:0")
else:
self.device = torch.device("cpu")
dfloat_cpu = torch.FloatTensor
dfloat_gpu = torch.cuda.FloatTensor
dlong_cpu = torch.LongTensor
dlong_gpu = torch.cuda.LongTensor
duint_cpu = torch.ByteTensor
dunit_gpu = torch.cuda.ByteTensor
dtype = torch.cuda.FloatTensor if torch.cuda.is_available(
) else torch.FloatTensor
dlongtype = torch.cuda.LongTensor if torch.cuda.is_available(
) else torch.LongTensor
duinttype = torch.cuda.ByteTensor if torch.cuda.is_available(
) else torch.ByteTensor
self.dtype = dtype
self.dlongtype = dlongtype
self.duinttype = duinttype
Q = DQN(in_channels=4, num_actions=num_options).type(dtype)
Q_t = DQN(in_channels=4, num_actions=num_options).type(dtype)
Q_t.load_state_dict(Q.state_dict())
Q_t.eval()
for param in Q_t.parameters():
param.requires_grad = False
Q = Q.to(self.device)
Q_t = Q_t.to(self.device)
self.batch_size = batch_size
self.Q = Q
self.Q_t = Q_t
# optimizer
optimizer = optim.RMSprop(Q.parameters(), lr=lr, alpha=alpha, eps=eps)
self.optimizer = optimizer
print('init: Meta Controller --> OK')
class ParallelNashAgent():
def __init__(self, env, id, args):
super(ParallelNashAgent, self).__init__()
self.id = id
self.current_model = DQN(env, args).to(args.device)
self.target_model = DQN(env, args).to(args.device)
update_target(self.current_model, self.target_model)
if args.load_model and os.path.isfile(args.load_model):
self.load_model(model_path)
self.epsilon_by_frame = epsilon_scheduler(args.eps_start,
args.eps_final,
args.eps_decay)
self.replay_buffer = ParallelReplayBuffer(args.buffer_size)
self.rl_optimizer = optim.Adam(self.current_model.parameters(),
lr=args.lr)
def save_model(self, model_path):
torch.save(self.current_model.state_dict(),
model_path + f'/{self.id}_dqn')
torch.save(self.target_model.state_dict(),
model_path + f'/{self.id}_dqn_target')
def load_model(self, model_path, eval=False, map_location=None):
self.current_model.load_state_dict(
torch.load(model_path + f'/{self.id}_dqn',
map_location=map_location))
self.target_model.load_state_dict(
torch.load(model_path + f'/{self.id}_dqn_target',
map_location=map_location))
if eval:
self.current_model.eval()
self.target_model.eval()
def run():
policy_net = DQN(num_channels, 19).cuda()
target_net = DQN(num_channels, 19).cuda()
optimizer = optim.Adam(policy_net.parameters(), LR)
memory = Memory(50000)
env = gym.make(ENV_NAME)
env.make_interactive(port=6666, realtime=False)
max_epi = 100
n_step = 2
update_period = 10
gamma = 0.99
total_steps = 0
epsilon = 0.95
endEpsilon = 0.01
stepDrop = (epsilon - endEpsilon) / max_epi
for num_epi in range(max_epi):
obs = env.reset()
state = converter(ENV_NAME, obs).cuda()
state = state.float()
done = False
total_reward = 0
steps = 0
if epsilon > endEpsilon:
epsilon -= stepDrop
while not done:
steps += 1
total_steps += 1
a_out = policy_net.sample_action(state, epsilon)
action_index = a_out
action = make_19action(env, action_index)
obs_prime, reward, done, info = env.step(action)
total_reward += reward
if done:
print("%d episode is done" % num_epi)
print("total rewards : %d " % total_reward)
writer.add_scalar('Rewards/train', total_reward, num_epi)
break
state_prime = converter(ENV_NAME, obs_prime).cuda()
append_sample(memory, policy_net, target_net, state, action_index,
reward, state_prime, done)
state = state_prime
if memory.size() > 1000:
update_network(policy_net, target_net, memory, 2, optimizer,
total_steps)
if total_steps % 2000 == 0:
update_target(policy_net, target_net)
class DQNTrainer():
def __init__(self, env, args):
super(DQNTrainer).__init__()
self.model = DQN(env, args, Nash=False).to(args.device)
self.target = DQN(env, args, Nash=False).to(args.device)
self.replay_buffer = ReplayBuffer(args.buffer_size)
self.optimizer = optim.Adam(self.model.parameters(), lr=args.lr)
self.args = args
def push(self, s, a, r, s_, d):
self.replay_buffer.push(s, a, r, s_, np.float32(d))
def update(self):
state, action, reward, next_state, done = self.replay_buffer.sample(
self.args.batch_size)
state = torch.FloatTensor(np.float32(state)).to(self.args.device)
next_state = torch.FloatTensor(np.float32(next_state)).to(
self.args.device)
action = torch.LongTensor(action).to(self.args.device)
reward = torch.FloatTensor(reward).to(self.args.device)
done = torch.FloatTensor(done).to(self.args.device)
# Q-Learning with target network
q_values = self.model(state)
target_next_q_values = self.target(next_state)
q_value = q_values.gather(1, action.unsqueeze(1)).squeeze(1)
next_q_value = target_next_q_values.max(1)[0]
expected_q_value = reward + (
self.args.gamma**self.args.multi_step) * next_q_value * (1 - done)
# Huber Loss
loss = F.smooth_l1_loss(q_value,
expected_q_value.detach(),
reduction='none')
loss = loss.mean()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return loss.item()
def act(self, s, args):
return self.model.act(s, args)
def save_model(self, model_path):
torch.save(self.model.state_dict(), model_path + 'dqn')
torch.save(self.target.state_dict(), model_path + 'dqn_target')
def train_setting(env, device):
init_screen = get_screen(env, device)
_, _, screen_height, screen_width = init_screen.shape
# Get number of actions from gym action space
n_actions = env.action_space.n
policy_net = DQN(screen_height, screen_width, n_actions).to(device)
target_net = DQN(screen_height, screen_width, n_actions).to(device)
target_net.load_state_dict(policy_net.state_dict())
target_net.eval()
optimizer = optim.RMSprop(policy_net.parameters())
memory = ReplayMemory(10000)
return n_actions, policy_net, target_net, optimizer, memory
class Agent():
def __init__(self, args, env):
self.args = args
self.action_space = env.action_space()
self.atoms = args.atoms
self.Vmin = args.V_min
self.Vmax = args.V_max
self.support = torch.linspace(args.V_min, args.V_max, self.atoms).to(
device=args.device) # Support (range) of z
self.delta_z = (args.V_max - args.V_min) / (self.atoms - 1)
self.batch_size = args.batch_size
self.n = args.multi_step
self.discount = args.discount
self.norm_clip = args.norm_clip
self.coeff = 0.01 if args.game in [
'pong', 'boxing', 'private_eye', 'freeway'
] else 1.
self.online_net = DQN(args, self.action_space).to(device=args.device)
self.momentum_net = DQN(args, self.action_space).to(device=args.device)
# self.predictor = prediction_MLP(in_dim=128, hidden_dim=128, out_dim=128)
if args.model: # Load pretrained model if provided
if os.path.isfile(args.model):
state_dict = torch.load(
args.model, map_location='cpu'
) # Always load tensors onto CPU by default, will shift to GPU if necessary
if 'conv1.weight' in state_dict.keys():
for old_key, new_key in (('conv1.weight',
'convs.0.weight'),
('conv1.bias', 'convs.0.bias'),
('conv2.weight',
'convs.2.weight'),
('conv2.bias', 'convs.2.bias'),
('conv3.weight',
'convs.4.weight'),
('conv3.bias', 'convs.4.bias')):
state_dict[new_key] = state_dict[
old_key] # Re-map state dict for old pretrained models
del state_dict[
old_key] # Delete old keys for strict load_state_dict
self.online_net.load_state_dict(state_dict)
print("Loading pretrained model: " + args.model)
else: # Raise error if incorrect model path provided
raise FileNotFoundError(args.model)
self.online_net.train()
# self.pred.train()
self.initialize_momentum_net()
self.momentum_net.train()
self.target_net = DQN(args, self.action_space).to(device=args.device)
self.update_target_net()
self.target_net.train()
for param in self.target_net.parameters():
param.requires_grad = False
for param in self.momentum_net.parameters():
param.requires_grad = False
self.optimiser = optim.Adam(self.online_net.parameters(),
lr=args.learning_rate,
eps=args.adam_eps)
# Resets noisy weights in all linear layers (of online net only)
def reset_noise(self):
self.online_net.reset_noise()
# Acts based on single state (no batch)
def act(self, state):
with torch.no_grad():
a, _, _ = self.online_net(state.unsqueeze(0))
return (a * self.support).sum(2).argmax(1).item()
# Acts with an ε-greedy policy (used for evaluation only)
def act_e_greedy(
self,
state,
epsilon=0.001): # High ε can reduce evaluation scores drastically
return np.random.randint(
0, self.action_space
) if np.random.random() < epsilon else self.act(state)
def learn(self, mem):
# Sample transitions
idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample(
self.batch_size)
# print('\n\n---------------')
# print(f'idxs: {idxs}, ')
# print(f'states: {states.shape}, ')
# print(f'actions: {actions.shape}, ')
# print(f'returns: {returns.shape}, ')
# print(f'next_states: {next_states.shape}, ')
# print(f'nonterminals: {nonterminals.shape}, ')
# print(f'weights: {weights.shape},')
aug_states_1 = aug(states).to(device=self.args.device)
aug_states_2 = aug(states).to(device=self.args.device)
# print(f'aug_states_1: {aug_states_1.shape}')
# print(f'aug_states_2: {aug_states_2.shape}')
# Calculate current state probabilities (online network noise already sampled)
log_ps, _, _ = self.online_net(
states, log=True) # Log probabilities log p(s_t, ·; θonline)
_, z_1, p_1 = self.online_net(aug_states_1, log=True)
_, z_2, p_2 = self.online_net(aug_states_2, log=True)
# p_1, p_2 = self.pred(z_1), self.pred(z_2)
# with torch.no_grad():
# p_2 = self.pred(z_2)
simsiam_loss = 2 + D(p_1, z_2) / 2 + D(p_2, z_1) / 2
# simsiam_loss = p_1.mean() + p_2.mean()
# simsiam_loss = p_1.mean() * 128
# simsiam_loss = - F.cosine_similarity(p_1, z_2.detach(), dim=-1).mean()
# print(simsiam_loss)
# simsiam_loss = 0
# _, z_target = self.momentum_net(aug_states_2, log=True) #z_k
# z_proj = torch.matmul(self.online_net.W, z_target.T)
# logits = torch.matmul(z_anch, z_proj)
# logits = (logits - torch.max(logits, 1)[0][:, None])
# logits = logits * 0.1
# labels = torch.arange(logits.shape[0]).long().to(device=self.args.device)
# moco_loss = (nn.CrossEntropyLoss()(logits, labels)).to(device=self.args.device)
log_ps_a = log_ps[range(self.batch_size),
actions] # log p(s_t, a_t; θonline)
# print(f'z_1: {z_1.shape}')
# print(f'p_1: {p_1.shape}')
# print('---------------\n\n')
# 1/0
with torch.no_grad():
# Calculate nth next state probabilities
pns, _, _ = self.online_net(
next_states) # Probabilities p(s_t+n, ·; θonline)
dns = self.support.expand_as(
pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θonline))
argmax_indices_ns = dns.sum(2).argmax(
1
) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))]
self.target_net.reset_noise() # Sample new target net noise
pns, _, _ = self.target_net(
next_states) # Probabilities p(s_t+n, ·; θtarget)
pns_a = pns[range(
self.batch_size
), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget)
# Compute Tz (Bellman operator T applied to z)
Tz = returns.unsqueeze(1) + nonterminals * (
self.discount**self.n) * self.support.unsqueeze(
0) # Tz = R^n + (γ^n)z (accounting for terminal states)
Tz = Tz.clamp(min=self.Vmin,
max=self.Vmax) # Clamp between supported values
# Compute L2 projection of Tz onto fixed support z
b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz
l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64)
# Fix disappearing probability mass when l = b = u (b is int)
l[(u > 0) * (l == u)] -= 1
u[(l < (self.atoms - 1)) * (l == u)] += 1
# Distribute probability of Tz
m = states.new_zeros(self.batch_size, self.atoms)
offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms),
self.batch_size).unsqueeze(1).expand(
self.batch_size,
self.atoms).to(actions)
m.view(-1).index_add_(
0, (l + offset).view(-1),
(pns_a *
(u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b)
m.view(-1).index_add_(
0, (u + offset).view(-1),
(pns_a *
(b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l)
loss = -torch.sum(
m * log_ps_a,
1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t)))
# loss = loss + (moco_loss * self.coeff)
loss = loss + (simsiam_loss * self.coeff)
self.online_net.zero_grad()
# self.pred.zero_grad()
curl_loss = (weights * loss).mean()
# print(curl_loss)
curl_loss.mean().backward(
) # Backpropagate importance-weighted minibatch loss
clip_grad_norm_(self.online_net.parameters(),
self.norm_clip) # Clip gradients by L2 norm
self.optimiser.step()
mem.update_priorities(idxs,
loss.detach().cpu().numpy()
) # Update priorities of sampled transitions
def learn_old(self, mem):
# Sample transitions
idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample(
self.batch_size)
# print('\n\n---------------')
# print(f'idxs: {idxs}, ')
# print(f'states: {states.shape}, ')
# print(f'actions: {actions.shape}, ')
# print(f'returns: {returns.shape}, ')
# print(f'next_states: {next_states.shape}, ')
# print(f'nonterminals: {nonterminals.shape}, ')
# print(f'weights: {weights.shape},')
aug_states_1 = aug(states).to(device=self.args.device)
aug_states_2 = aug(states).to(device=self.args.device)
# print(f'aug_states_1: {aug_states_1.shape}')
# print(f'aug_states_2: {aug_states_2.shape}')
# Calculate current state probabilities (online network noise already sampled)
log_ps, _, _ = self.online_net(
states, log=True) # Log probabilities log p(s_t, ·; θonline)
_, z_anch, _ = self.online_net(aug_states_1, log=True) #z_q
_, z_target, _ = self.momentum_net(aug_states_2, log=True) #z_k
z_proj = torch.matmul(self.online_net.W, z_target.T)
logits = torch.matmul(z_anch, z_proj)
logits = (logits - torch.max(logits, 1)[0][:, None])
logits = logits * 0.1
labels = torch.arange(
logits.shape[0]).long().to(device=self.args.device)
moco_loss = (nn.CrossEntropyLoss()(logits,
labels)).to(device=self.args.device)
log_ps_a = log_ps[range(self.batch_size),
actions] # log p(s_t, a_t; θonline)
# print(f'z_anch: {z_anch.shape}')
# print(f'z_target: {z_target.shape}')
# print(f'z_proj: {z_proj.shape}')
# print(f'logits: {logits.shape}')
# print(logits)
# print(f'labels: {labels.shape}')
# print(labels)
# print('---------------\n\n')
# 1/0
with torch.no_grad():
# Calculate nth next state probabilities
pns, _, _ = self.online_net(
next_states) # Probabilities p(s_t+n, ·; θonline)
dns = self.support.expand_as(
pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θonline))
argmax_indices_ns = dns.sum(2).argmax(
1
) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))]
self.target_net.reset_noise() # Sample new target net noise
pns, _, _ = self.target_net(
next_states) # Probabilities p(s_t+n, ·; θtarget)
pns_a = pns[range(
self.batch_size
), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget)
# Compute Tz (Bellman operator T applied to z)
Tz = returns.unsqueeze(1) + nonterminals * (
self.discount**self.n) * self.support.unsqueeze(
0) # Tz = R^n + (γ^n)z (accounting for terminal states)
Tz = Tz.clamp(min=self.Vmin,
max=self.Vmax) # Clamp between supported values
# Compute L2 projection of Tz onto fixed support z
b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz
l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64)
# Fix disappearing probability mass when l = b = u (b is int)
l[(u > 0) * (l == u)] -= 1
u[(l < (self.atoms - 1)) * (l == u)] += 1
# Distribute probability of Tz
m = states.new_zeros(self.batch_size, self.atoms)
offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms),
self.batch_size).unsqueeze(1).expand(
self.batch_size,
self.atoms).to(actions)
m.view(-1).index_add_(
0, (l + offset).view(-1),
(pns_a *
(u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b)
m.view(-1).index_add_(
0, (u + offset).view(-1),
(pns_a *
(b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l)
loss = -torch.sum(
m * log_ps_a,
1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t)))
print(moco_loss)
loss = loss + (moco_loss * self.coeff)
self.online_net.zero_grad()
curl_loss = (weights * loss).mean()
curl_loss.mean().backward(
) # Backpropagate importance-weighted minibatch loss
clip_grad_norm_(self.online_net.parameters(),
self.norm_clip) # Clip gradients by L2 norm
self.optimiser.step()
mem.update_priorities(idxs,
loss.detach().cpu().numpy()
) # Update priorities of sampled transitions
def update_target_net(self):
self.target_net.load_state_dict(self.online_net.state_dict())
def initialize_momentum_net(self):
for param_q, param_k in zip(self.online_net.parameters(),
self.momentum_net.parameters()):
param_k.data.copy_(param_q.data) # update
param_k.requires_grad = False # not update by gradient
# Code for this function from https://github.com/facebookresearch/moco
@torch.no_grad()
def update_momentum_net(self, momentum=0.999):
for param_q, param_k in zip(self.online_net.parameters(),
self.momentum_net.parameters()):
param_k.data.copy_(momentum * param_k.data +
(1. - momentum) * param_q.data) # update
# Save model parameters on current device (don't move model between devices)
def save(self, path, name='model.pth'):
torch.save(self.online_net.state_dict(), os.path.join(path, name))
# Evaluates Q-value based on single state (no batch)
def evaluate_q(self, state):
with torch.no_grad():
a, _, _ = self.online_net(state.unsqueeze(0))
return (a * self.support).sum(2).max(1)[0].item()
def train(self):
self.online_net.train()
def eval(self):
self.online_net.eval()
class Agent():
def __init__(self, args, env):
self.action_space = env.action_space()
self.batch_size = args.batch_size
self.discount = args.discount
self.max_gradient_norm = args.max_gradient_norm
self.policy_net = DQN(args, self.action_space)
if args.model and os.path.isfile(args.model):
self.policy_net.load_state_dict(torch.load(args.model))
self.policy_net.train()
self.target_net = DQN(args, self.action_space)
self.update_target_net()
self.target_net.eval()
self.optimiser = optim.Adam(self.policy_net.parameters(), lr=args.lr)
def act(self, state, epsilon):
if random.random() > epsilon:
return self.policy_net(state.unsqueeze(0)).max(1)[1].data[0]
else:
return random.randint(0, self.action_space - 1)
def learn(self, mem):
transitions = mem.sample(self.batch_size)
batch = Transition(*zip(*transitions)) # Transpose the batch
states = Variable(torch.stack(batch.state, 0))
actions = Variable(torch.LongTensor(batch.action).unsqueeze(1))
rewards = Variable(torch.Tensor(batch.reward))
non_final_mask = torch.ByteTensor(
tuple(map(
lambda s: s is not None,
batch.next_state))) # Only process non-terminal next states
next_states = Variable(
torch.stack(tuple(s for s in batch.next_state if s is not None),
0),
volatile=True
) # Prevent backpropagating through expected action values
Qs = self.policy_net(states).gather(1, actions) # Q(s_t, a_t; θpolicy)
next_state_argmax_indices = self.policy_net(next_states).max(
1, keepdim=True
)[1] # Perform argmax action selection using policy network: argmax_a[Q(s_t+1, a; θpolicy)]
Qns = Variable(torch.zeros(
self.batch_size)) # Q(s_t+1, a) = 0 if s_t+1 is terminal
Qns[non_final_mask] = self.target_net(next_states).gather(
1, next_state_argmax_indices
) # Q(s_t+1, argmax_a[Q(s_t+1, a; θpolicy)]; θtarget)
Qns.volatile = False # Remove volatile flag to prevent propagating it through loss
target = rewards + (
self.discount * Qns
) # Double-Q target: Y = r + γ.Q(s_t+1, argmax_a[Q(s_t+1, a; θpolicy)]; θtarget)
loss = F.smooth_l1_loss(
Qs, target) # Huber loss on TD-error δ: δ = Y - Q(s_t, a_t)
# TODO: TD-error clipping?
self.policy_net.zero_grad()
loss.backward()
nn.utils.clip_grad_norm(self.policy_net.parameters(),
self.max_gradient_norm) # Clamp gradients
self.optimiser.step()
def update_target_net(self):
self.target_net.load_state_dict(self.policy_net.state_dict())
def save(self, path):
torch.save(self.policy_net.state_dict(),
os.path.join(path, 'model.pth'))
def evaluate_q(self, state):
return self.policy_net(state.unsqueeze(0)).max(1)[0].data[0]
def train(self):
self.policy_net.train()
def eval(self):
self.policy_net.eval()
class Agent():
def __init__(self, args, env):
self.action_space = env.action_space()
self.atoms = args.atoms
self.Vmin = args.V_min
self.Vmax = args.V_max
self.support = torch.linspace(args.V_min, args.V_max, self.atoms).to(
device=args.device) # Support (range) of z
self.delta_z = (args.V_max - args.V_min) / (self.atoms - 1)
self.batch_size = args.batch_size
self.n = args.multi_step
self.discount = args.discount
self.online_net = DQN(args, self.action_space).to(device=args.device)
if args.model and os.path.isfile(args.model):
# Always load tensors onto CPU by default, will shift to GPU if necessary
self.online_net.load_state_dict(
torch.load(args.model, map_location='cpu'))
self.online_net.train()
self.target_net = DQN(args, self.action_space).to(device=args.device)
self.update_target_net()
self.target_net.train()
for param in self.target_net.parameters():
param.requires_grad = False
self.optimiser = optim.Adam(self.online_net.parameters(),
lr=args.lr,
eps=args.adam_eps)
# Resets noisy weights in all linear layers (of online net only)
def reset_noise(self):
self.online_net.reset_noise()
# Acts based on single state (no batch)
def act(self, state):
with torch.no_grad():
return (self.online_net(state.unsqueeze(0)) *
self.support).sum(2).argmax(1).item()
# Acts with an ε-greedy policy (used for evaluation only)
def act_e_greedy(
self,
state,
epsilon=0.001): # High ε can reduce evaluation scores drastically
return random.randrange(
self.action_space) if random.random() < epsilon else self.act(
state)
def learn(self, mem):
# Sample transitions
idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample(
self.batch_size)
# Calculate current state probabilities (online network noise already sampled)
log_ps = self.online_net(
states, log=True) # Log probabilities log p(s_t, ·; θonline)
log_ps_a = log_ps[range(self.batch_size),
actions] # log p(s_t, a_t; θonline)
with torch.no_grad():
# Calculate nth next state probabilities
pns = self.online_net(
next_states) # Probabilities p(s_t+n, ·; θonline)
dns = self.support.expand_as(
pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θonline))
argmax_indices_ns = dns.sum(2).argmax(
1
) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))]
self.target_net.reset_noise() # Sample new target net noise
pns = self.target_net(
next_states) # Probabilities p(s_t+n, ·; θtarget)
pns_a = pns[range(
self.batch_size
), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget)
# Compute Tz (Bellman operator T applied to z)
Tz = returns.unsqueeze(1) + nonterminals * (
self.discount**self.n) * self.support.unsqueeze(
0) # Tz = R^n + (γ^n)z (accounting for terminal states)
Tz = Tz.clamp(min=self.Vmin,
max=self.Vmax) # Clamp between supported values
# Compute L2 projection of Tz onto fixed support z
b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz
l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64)
# Fix disappearing probability mass when l = b = u (b is int)
l[(u > 0) * (l == u)] -= 1
u[(l < (self.atoms - 1)) * (l == u)] += 1
# Distribute probability of Tz
m = states.new_zeros(self.batch_size, self.atoms)
offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms),
self.batch_size).unsqueeze(1).expand(
self.batch_size,
self.atoms).to(actions)
m.view(-1).index_add_(
0, (l + offset).view(-1),
(pns_a *
(u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b)
m.view(-1).index_add_(
0, (u + offset).view(-1),
(pns_a *
(b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l)
loss = -torch.sum(
m * log_ps_a,
1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t)))
self.online_net.zero_grad()
(weights * loss).mean().backward(
) # Backpropagate importance-weighted minibatch loss
self.optimiser.step()
mem.update_priorities(
idxs, loss.detach()) # Update priorities of sampled transitions
def update_target_net(self):
self.target_net.load_state_dict(self.online_net.state_dict())
# Save model parameters on current device (don't move model between devices)
def save(self, path):
torch.save(self.online_net.state_dict(),
os.path.join(path, 'model.pth'))
# Evaluates Q-value based on single state (no batch)
def evaluate_q(self, state):
with torch.no_grad():
return (self.online_net(state.unsqueeze(0)) *
self.support).sum(2).max(1)[0].item()
def train(self):
self.online_net.train()
def eval(self):
self.online_net.eval()
device0 = torch.device("cuda:0")
else:
device0 = torch.device("cpu")
dtype = torch.cuda.FloatTensor if torch.cuda.is_available(
) else torch.FloatTensor
dlongtype = torch.cuda.LongTensor if torch.cuda.is_available(
) else torch.LongTensor
duinttype = torch.cuda.ByteTensor if torch.cuda.is_available(
) else torch.ByteTensor
Qt = DQN(in_channels=5, num_actions=18).type(dtype)
Qt_t = DQN(in_channels=5, num_actions=18).type(dtype)
Qt_t.load_state_dict(Qt.state_dict())
Qt_t.eval()
for param in Qt_t.parameters():
param.requires_grad = False
if torch.cuda.device_count() > 0:
Qt = nn.DataParallel(Qt).to(device0)
Qt_t = nn.DataParallel(Qt_t).to(device0)
batch_size = BATCH_SIZE * torch.cuda.device_count()
else:
batch_size = BATCH_SIZE
# optimizer
optimizer = optim.RMSprop(Qt.parameters(),
lr=LEARNING_RATE,
alpha=ALPHA,
eps=EPS)
class DQNAgent:
"""
Interacts with and learns from the environment.
Vanilla DQN.
"""
def __init__(self, state_size: int, action_size: int, seed: int):
"""
Initialize an Agent object.
:param state_size: dimension of each state;
:param action_size: dimension of each action;
:param seed: random seed.
"""
self.state_size = state_size
self.action_size = action_size
random.seed(seed)
# Q-Network
self.network_local = DQN(state_size, action_size, seed).to(DEVICE)
self.network_target = DQN(state_size, action_size, seed).to(DEVICE)
self.optimizer = optim.Adam(self.network_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action: int, reward: float, next_state, done):
"""
Save experiences in the replay memory and check if it's time to learn.
:param state: (array_like) current state;
:param action: action taken;
:param reward: reward received;
:param next_state: (array_like) next state;
:param done: terminal state indicator; int or bool.
"""
# Save experience in replay memory
self.memory.push(state, action, reward, next_state, done)
# Increment time step and compare it to the network update frequency
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# Check if there is enough samples in the memory to learn
if len(self.memory) > BATCH_SIZE:
# sample experiences from memory
experiences = self.memory.sample()
# learn from sampled experiences
self.learn(experiences, GAMMA)
def act(self, state, eps: float = 0.):
"""
Returns actions for given state as per current policy.
:param state: (array_like) current state
:param eps: epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(DEVICE)
self.network_local.eval()
with torch.no_grad():
action_values = self.network_local(state)
self.network_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma: float):
"""
Update value parameters using given batch of experience tuples.
:param experiences: (Tuple[torch.Tensor]) tuple of (s, a, r, s', done) tuples;
:param gamma: discount factor.
"""
states, actions, rewards, next_states, dones = experiences
# Get max predicted Q values (for next states) from target model
Q_targets_next = self.network_target(next_states).detach().max(
1)[0].unsqueeze(1)
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Get expected Q values from local model
Q_expected = self.network_local(states).gather(1, actions)
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.network_local, self.network_target, TAU)
@staticmethod
def soft_update(local_model, target_model, tau: float):
"""
Soft update model parameters,
θ_target = τ*θ_local + (1 - τ)*θ_target.
:param local_model: (PyTorch model) weights will be copied from;
:param target_model: (PyTorch model) weights will be copied to;
:param tau: 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 DDQNAgent:
def __init__(self, config: Config, training=True):
self.config = config
self.is_training = training
self.buffer = ReplayBuffer(self.config.max_buff)
self.model = DQN(self.config.state_shape, self.config.action_dim)
self.target_model = DQN(self.config.state_shape,
self.config.action_dim)
self.target_model.load_state_dict(self.model.state_dict())
self.optim = Adam(self.model.parameters(),
lr=self.config.learning_rate)
self.model.cuda()
self.target_model.cuda()
def act(self, state, epsilon=None):
if epsilon is None: epsilon = self.config.epsilon_min
if random.random() > epsilon or not self.is_training:
state = torch.tensor(state, dtype=torch.float).unsqueeze(0)
state = state.cuda()
q_value = self.model.forward(state)
action = q_value.max(1)[1].item()
else:
action = random.randrange(self.config.action_dim)
return action
def learn(self, t):
s, a, r, s2, done = self.buffer.sample(self.config.batch_size)
s = torch.tensor(s, dtype=torch.float)
a = torch.tensor(a, dtype=torch.long)
r = torch.tensor(r, dtype=torch.float)
s2 = torch.tensor(s2, dtype=torch.float)
done = torch.tensor(done, dtype=torch.float)
s = s.cuda()
a = a.cuda()
r = r.cuda()
s2 = s2.cuda()
done = done.cuda()
q_values = self.model(s).cuda()
next_q_values = self.model(s2).cuda()
next_q_state_values = self.target_model(s2).cuda()
q_value = q_values.gather(1, a.unsqueeze(1)).squeeze(1)
next_q_value = next_q_state_values.gather(
1,
next_q_values.max(1)[1].unsqueeze(1)).squeeze(1)
expected_q_value = r + self.config.gamma * next_q_value * (1 - done)
loss = (q_value - expected_q_value.detach()).pow(2).mean()
self.optim.zero_grad()
loss.backward()
self.optim.step()
if t % self.config.update_interval == 0:
self.target_model.load_state_dict(self.model.state_dict())
return loss.item()
def load_weights(self, model_path):
model = torch.load(model_path)
if 'model' in model:
self.model.load_state_dict(model['model'])
else:
self.model.load_state_dict(model)
def save_checkpoint(self):
os.makedirs('ckpt', exist_ok=True)
torch.save(self.model.state_dict(), 'ckpt/model.pt')
def load_checkpoint(self):
self.model.load_state_dict('ckpt/model.pt')
self.target_model.load_state_dict('ckpt/model.pt')
class Learner:
def __init__(self, network, batch_size):
self.learner_network = DQN(19).cuda().float()
self.learner_target_network = DQN(19).cuda().float()
self.learner_network.load_state_dict(network.state_dict())
self.learner_target_network.load_state_dict(network.state_dict())
self.shared_network = DQN(19).cpu()
self.count = 0
self.batch_size = batch_size
wandb.init(project='apex_dqfd_Learner', entity='neverparadise')
# 1. sampling
# 2. calculate gradient
# 3. weight update
# 4. compute priorities
# 5. priorities of buffer update
# 6. remove old memory
def count(self):
return self.count
def get_network(self):
self.shared_network.load_state_dict(self.learner_network.state_dict())
return self.shared_network
def update_network(self, memory, demos, batch_size, optimizer, actor):
while(ray.get(actor.get_counter.remote()) < 100):
print("update_network")
agent_batch, agent_idxs, agent_weights = ray.get(memory.sample.remote(batch_size))
demo_batch, demo_idxs, demo_weights = ray.get(demos.sample.remote(batch_size))
# demo_batch = (batch_size, state, action, reward, next_state, done, n_rewards)
# print(len(demo_batch[0])) # 0번째 배치이므로 0이 나옴
state_list = []
action_list = []
reward_list = []
next_state_list = []
done_mask_list = []
n_rewards_list = []
for agent_transition in agent_batch:
s, a, r, s_prime, done_mask, n_rewards = agent_transition
state_list.append(s)
action_list.append([a])
reward_list.append([r])
next_state_list.append(s_prime)
done_mask_list.append([done_mask])
n_rewards_list.append([n_rewards])
for expert_transition in demo_batch:
s, a, r, s_prime, done_mask, n_rewards = expert_transition
state_list.append(s)
action_list.append([a])
reward_list.append([r])
next_state_list.append(s_prime)
done_mask_list.append([done_mask])
n_rewards_list.append([n_rewards])
s = torch.stack(state_list).float().cuda()
a = torch.tensor(action_list, dtype=torch.int64).cuda()
r = torch.tensor(reward_list).cuda()
s_prime = torch.stack(next_state_list).float().cuda()
done_mask = torch.tensor(done_mask_list).float().cuda()
nr = torch.tensor(n_rewards_list).float().cuda()
q_vals = self.learner_network(s)
state_action_values = q_vals.gather(1, a)
# comparing the q values to the values expected using the next states and reward
next_state_values = self.learner_target_network(s_prime).max(1)[0].unsqueeze(1)
target = r + (next_state_values * gamma * done_mask)
# calculating the q loss, n-step return lossm supervised_loss
is_weights = torch.FloatTensor(agent_weights).to(device)
q_loss = (is_weights * F.mse_loss(state_action_values, target)).mean()
n_step_loss = (state_action_values.max(1)[0] + nr).mean()
supervised_loss = margin_loss(q_vals, a, 1, 1)
loss = q_loss + supervised_loss + n_step_loss
errors = torch.abs(state_action_values - target).data.cpu().detach()
errors = errors.numpy()
# update priority
for i in range(batch_size):
idx = agent_idxs[i]
memory.update.remote(idx, errors[i])
# optimization step and logging
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm(self.learner_network.parameters(), 100)
optimizer.step()
torch.save(self.learner_network.state_dict(), model_path + "apex_dqfd_learner.pth")
self.count +=1
if(self.count % 20 == 0 and self.count != 0):
self.update_target_networks()
print("leaner_network updated")
return loss
def update_target_networks(self):
self.learner_target_network.load_state_dict(self.learner_network.state_dict())
print("leaner_target_network updated")
class Agent():
def __init__(self, args, env):
self.action_space = env.action_space()
self.atoms = args.atoms
self.Vmin = args.V_min
self.Vmax = args.V_max
self.support = torch.linspace(args.V_min, args.V_max,
args.atoms) # Support (range) of z
self.delta_z = (args.V_max - args.V_min) / (args.atoms - 1)
self.batch_size = args.batch_size
self.n = args.multi_step
self.discount = args.discount
self.online_net = DQN(args, self.action_space)
if args.model and os.path.isfile(args.model):
self.online_net.load_state_dict(
torch.load(args.model, map_location='cpu'))
self.online_net.train()
self.target_net = DQN(args, self.action_space)
self.update_target_net()
self.target_net.train()
for param in self.target_net.parameters():
param.requires_grad = False
self.optimiser = optim.Adam(self.online_net.parameters(),
lr=args.lr,
eps=args.adam_eps)
if args.cuda:
self.online_net.cuda()
self.target_net.cuda()
self.support = self.support.cuda()
# Resets noisy weights in all linear layers (of online net only)
def reset_noise(self):
self.online_net.reset_noise()
# Acts based on single state (no batch)
def act(self, state):
return (self.online_net(state.unsqueeze(0)).data *
self.support).sum(2).max(1)[1][0]
# Acts with an ε-greedy policy
def act_e_greedy(self, state, epsilon=0.001):
return random.randrange(
self.action_space) if random.random() < epsilon else self.act(
state)
def learn(self, mem):
# Sample transitions
idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample(
self.batch_size)
# Calculate current state probabilities
self.online_net.reset_noise() # Sample new noise for online network
ps = self.online_net(states) # Probabilities p(s_t, ·; θonline)
ps_a = ps[range(self.batch_size), actions] # p(s_t, a_t; θonline)
# Calculate nth next state probabilities
self.online_net.reset_noise() # Sample new noise for action selection
pns = self.online_net(
next_states).data # Probabilities p(s_t+n, ·; θonline)
dns = self.support.expand_as(
pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θonline))
argmax_indices_ns = dns.sum(2).max(
1
)[1] # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))]
self.target_net.reset_noise() # Sample new target net noise
pns = self.target_net(
next_states).data # Probabilities p(s_t+n, ·; θtarget)
pns_a = pns[range(
self.batch_size
), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget)
# Compute Tz (Bellman operator T applied to z)
Tz = returns.unsqueeze(1) + nonterminals * (
self.discount**self.n) * self.support.unsqueeze(
0) # Tz = R^n + (γ^n)z (accounting for terminal states)
Tz = Tz.clamp(min=self.Vmin,
max=self.Vmax) # Clamp between supported values
# Compute L2 projection of Tz onto fixed support z
b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz
l, u = b.floor().long(), b.ceil().long()
# Fix disappearing probability mass when l = b = u (b is int)
l[(u > 0) * (l == u)] -= 1
u[(l < (self.atoms - 1)) * (l == u)] += 1
# Distribute probability of Tz
m = states.data.new(self.batch_size, self.atoms).zero_()
offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms),
self.batch_size).unsqueeze(1).expand(
self.batch_size,
self.atoms).type_as(actions)
m.view(-1).index_add_(
0, (l + offset).view(-1),
(pns_a *
(u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b)
m.view(-1).index_add_(
0, (u + offset).view(-1),
(pns_a *
(b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l)
ps_a = ps_a.clamp(min=1e-3) # Clamp for numerical stability in log
loss = -torch.sum(
Variable(m) * ps_a.log(),
1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t)))
self.online_net.zero_grad()
(weights * loss).mean().backward() # Importance weight losses
self.optimiser.step()
mem.update_priorities(
idxs, loss.data) # Update priorities of sampled transitions
def update_target_net(self):
self.target_net.load_state_dict(self.online_net.state_dict())
def save(self, path):
torch.save(self.online_net.state_dict(),
os.path.join(path, 'model.pth'))
# Evaluates Q-value based on single state (no batch)
def evaluate_q(self, state):
return (self.online_net(state.unsqueeze(0)).data *
self.support).sum(2).max(1)[0][0]
def train(self):
self.online_net.train()
def eval(self):
self.online_net.eval()
def train(env, args, writer):
p1_current_model = DQN(env, args).to(args.device)
p1_target_model = DQN(env, args).to(args.device)
update_target(p1_current_model, p1_target_model)
p2_current_model = DQN(env, args).to(args.device)
p2_target_model = DQN(env, args).to(args.device)
update_target(p2_current_model, p2_target_model)
if args.noisy:
p1_current_model.update_noisy_modules()
p1_target_model.update_noisy_modules()
p2_current_model.update_noisy_modules()
p2_target_model.update_noisy_modules()
if args.load_model and os.path.isfile(args.load_model):
load_model(p1_current_model, args, 1)
load_model(p2_current_model, args, 2)
epsilon_by_frame = epsilon_scheduler(args.eps_start, args.eps_final, args.eps_decay)
beta_by_frame = beta_scheduler(args.beta_start, args.beta_frames)
if args.prioritized_replay:
p1_replay_buffer = PrioritizedReplayBuffer(args.buffer_size, args.alpha)
p2_replay_buffer = PrioritizedReplayBuffer(args.buffer_size, args.alpha)
else:
p1_replay_buffer = ReplayBuffer(args.buffer_size)
p2_replay_buffer = ReplayBuffer(args.buffer_size)
p1_state_deque = deque(maxlen=args.multi_step)
p2_state_deque = deque(maxlen=args.multi_step)
p1_reward_deque = deque(maxlen=args.multi_step)
p1_action_deque = deque(maxlen=args.multi_step)
p2_reward_deque = deque(maxlen=args.multi_step)
p2_action_deque = deque(maxlen=args.multi_step)
p1_optimizer = optim.Adam(p1_current_model.parameters(), lr=args.lr)
p2_optimizer = optim.Adam(p2_current_model.parameters(), lr=args.lr)
length_list = []
p1_reward_list, p1_loss_list = [], []
p2_reward_list, p2_loss_list = [], []
p1_episode_reward, p2_episode_reward = 0, 0
episode_length = 0
prev_time = time.time()
prev_frame = 1
(p1_state, p2_state) = env.reset()
for frame_idx in range(1, args.max_frames + 1):
if args.noisy:
p1_current_model.sample_noise()
p1_target_model.sample_noise()
p2_current_model.sample_noise()
p2_target_model.sample_noise()
epsilon = epsilon_by_frame(frame_idx)
p1_action = p1_current_model.act(torch.FloatTensor(p1_state).to(args.device), epsilon)
p2_action = p2_current_model.act(torch.FloatTensor(p2_state).to(args.device), epsilon)
if args.render:
env.render()
actions = {"1": p1_action, "2": p2_action}
(p1_next_state, p2_next_state), reward, done, _ = env.step(actions)
p1_state_deque.append(p1_state)
p2_state_deque.append(p2_state)
if args.negative:
p1_reward_deque.append(reward[0] - 1)
else:
p1_reward_deque.append(reward[0])
p1_action_deque.append(p1_action)
if args.negative:
p2_reward_deque.append(reward[1] - 1)
else:
p2_reward_deque.append(reward[1])
p2_action_deque.append(p2_action)
if len(p1_state_deque) == args.multi_step or done:
n_reward = multi_step_reward(p1_reward_deque, args.gamma)
n_state = p1_state_deque[0]
n_action = p1_action_deque[0]
p1_replay_buffer.push(n_state, n_action, n_reward, p1_next_state, np.float32(done))
n_reward = multi_step_reward(p2_reward_deque, args.gamma)
n_state = p2_state_deque[0]
n_action = p2_action_deque[0]
p2_replay_buffer.push(n_state, n_action, n_reward, p2_next_state, np.float32(done))
(p1_state, p2_state) = (p1_next_state, p2_next_state)
p1_episode_reward += (reward[0])
p2_episode_reward += (reward[1])
if args.negative:
p1_episode_reward -= 1
p2_episode_reward -= 1
episode_length += 1
if done or episode_length > args.max_episode_length:
(p1_state, p2_state) = env.reset()
p1_reward_list.append(p1_episode_reward)
p2_reward_list.append(p2_episode_reward)
length_list.append(episode_length)
writer.add_scalar("data/p1_episode_reward", p1_episode_reward, frame_idx)
writer.add_scalar("data/p2_episode_reward", p2_episode_reward, frame_idx)
writer.add_scalar("data/episode_length", episode_length, frame_idx)
p1_episode_reward, p2_episode_reward, episode_length = 0, 0, 0
p1_state_deque.clear()
p2_state_deque.clear()
p1_reward_deque.clear()
p2_reward_deque.clear()
p1_action_deque.clear()
p2_action_deque.clear()
if len(p1_replay_buffer) > args.learning_start and frame_idx % args.train_freq == 0:
beta = beta_by_frame(frame_idx)
loss = compute_td_loss(p1_current_model, p1_target_model, p1_replay_buffer, p1_optimizer, args, beta)
p1_loss_list.append(loss.item())
writer.add_scalar("data/p1_loss", loss.item(), frame_idx)
loss = compute_td_loss(p2_current_model, p2_target_model, p2_replay_buffer, p2_optimizer, args, beta)
p2_loss_list.append(loss.item())
writer.add_scalar("data/p2_loss", loss.item(), frame_idx)
if frame_idx % args.update_target == 0:
update_target(p1_current_model, p1_target_model)
update_target(p2_current_model, p2_target_model)
if frame_idx % args.evaluation_interval == 0:
print_log(frame_idx, prev_frame, prev_time, p1_reward_list, length_list, p1_loss_list)
print_log(frame_idx, prev_frame, prev_time, p2_reward_list, length_list, p2_loss_list)
p1_reward_list.clear(), p2_reward_list.clear(), length_list.clear()
p1_loss_list.clear(), p2_loss_list.clear()
prev_frame = frame_idx
prev_time = time.time()
save_model(p1_current_model, args, 1)
save_model(p2_current_model, args, 2)
save_model(p1_current_model, args, 1)
save_model(p2_current_model, args, 2)
adam_learning_rate = 0.000625
epsilon = 1
epsilon_max = 1
epsilon_min = .01
decay_rate = 0.005 # choosen to reach min in around 400-500 episodes
tau = .001 # for soft update, taken from (Continuous control with deep reinforcement learning)
buffer_size = 1000 # replay buffer size
memory_size = 72 # minimum memories needed for training
replaybuffer = deque(maxlen=buffer_size)
model = DQN(total_states, total_actions, seed)
target = DQN(total_states, total_actions, seed)
optimizer = optim.Adam(model.parameters(), lr=adam_learning_rate)
max_steps = 1000
max_episodes = 2000
episode = 0
frames = 0
target_update_rate = 4
scores = list()
cmr = list()
score_bucket = deque(maxlen=100)
score_bucket.append(0)
runtimes = list()
while episode < max_episodes and np.mean(score_bucket) < 200:
state = env.reset() # Restart the game
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if torch.cuda.is_available():
print('----- cuda available ----')
else:
print('----- cuda unavailable ----')
policy_net = DQN(output=4).to(device)
target_net = DQN(output=4).to(device)
target_net.load_state_dict(policy_net.state_dict())
optimizer = torch.optim.Adam(policy_net.parameters(), lr=lr)
env = gym.make('PongNoFrameskip-v4')
#env = gym.make('Pong-v0')
env = envwrapper.make_env(env)
# prepare memory
OPTIMIZE_THRESHOLD = 1000
capacity = OPTIMIZE_THRESHOLD * 10
replaymemory = memory.ReplayMemory(capacity)
episode_rewards = train(env, EPISODE_NUM)
plot_rewards(episode_rewards)
class Agent():
def __init__(self, args, env):
self.action_space = env.action_space()
self.atoms = args.atoms
self.Vmin = args.V_min
self.Vmax = args.V_max
self.support = torch.linspace(args.V_min, args.V_max, args.atoms) # Support (range) of z
self.delta_z = (args.V_max - args.V_min) / (args.atoms - 1)
self.batch_size = args.batch_size
self.n = args.multi_step
self.discount = args.discount
self.priority_exponent = args.priority_exponent
self.max_gradient_norm = args.max_gradient_norm
self.policy_net = DQN(args, self.action_space)
if args.model and os.path.isfile(args.model):
self.policy_net.load_state_dict(torch.load(args.model))
self.policy_net.train()
self.target_net = DQN(args, self.action_space)
self.update_target_net()
self.target_net.eval()
self.optimiser = optim.Adam(self.policy_net.parameters(), lr=args.lr, eps=args.adam_eps)
if args.cuda:
self.policy_net.cuda()
self.target_net.cuda()
self.support = self.support.cuda()
# Resets noisy weights in all linear layers (of policy and target nets)
def reset_noise(self):
self.policy_net.reset_noise()
self.target_net.reset_noise()
# Acts based on single state (no batch)
def act(self, state):
return (self.policy_net(state.unsqueeze(0)).data * self.support).sum(2).max(1)[1][0]
def learn(self, mem):
idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample(self.batch_size)
batch_size = len(idxs) # May return less than specified if invalid transitions sampled
# Calculate current state probabilities
ps = self.policy_net(states) # Probabilities p(s_t, ·; θpolicy)
ps_a = ps[range(batch_size), actions] # p(s_t, a_t; θpolicy)
# Calculate nth next state probabilities
pns = self.policy_net(next_states).data # Probabilities p(s_t+n, ·; θpolicy)
dns = self.support.expand_as(pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θpolicy))
argmax_indices_ns = dns.sum(2).max(1)[1] # Perform argmax action selection using policy network: argmax_a[(z, p(s_t+n, a; θpolicy))]
pns = self.target_net(next_states).data # Probabilities p(s_t+n, ·; θtarget)
pns_a = pns[range(batch_size), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θpolicy))]; θtarget)
pns_a *= nonterminals # Set p = 0 for terminal nth next states as all possible expected returns = expected reward at final transition
# Compute Tz (Bellman operator T applied to z)
Tz = returns.unsqueeze(1) + nonterminals * (self.discount ** self.n) * self.support.unsqueeze(0) # Tz = R^n + (γ^n)z (accounting for terminal states)
Tz = Tz.clamp(min=self.Vmin, max=self.Vmax) # Clamp between supported values
# Compute L2 projection of Tz onto fixed support z
b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz
l, u = b.floor().long(), b.ceil().long()
# Distribute probability of Tz
m = states.data.new(batch_size, self.atoms).zero_()
offset = torch.linspace(0, ((batch_size - 1) * self.atoms), batch_size).long().unsqueeze(1).expand(batch_size, self.atoms).type_as(actions)
m.view(-1).index_add_(0, (l + offset).view(-1), (pns_a * (u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b)
m.view(-1).index_add_(0, (u + offset).view(-1), (pns_a * (b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l)
loss = -torch.sum(Variable(m) * ps_a.log(), 1) # Cross-entropy loss (minimises Kullback-Leibler divergence)
self.policy_net.zero_grad()
(weights * loss).mean().backward() # Importance weight losses
nn.utils.clip_grad_norm(self.policy_net.parameters(), self.max_gradient_norm) # Clip gradients (normalising by max value of gradient L2 norm)
self.optimiser.step()
mem.update_priorities(idxs, loss.data.abs().pow(self.priority_exponent)) # Update priorities of sampled transitions
def update_target_net(self):
self.target_net.load_state_dict(self.policy_net.state_dict())
def save(self, path):
torch.save(self.policy_net.state_dict(), os.path.join(path, 'model.pth'))
# Evaluates Q-value based on single state (no batch)
def evaluate_q(self, state):
return (self.policy_net(state.unsqueeze(0)).data * self.support).sum(2).max(1)[0][0]
def train(self):
self.policy_net.train()
def eval(self):
self.policy_net.eval()
DEVICE = "cpu"
print(f"Using device: {DEVICE}")
print(f"Settings:\n{SETTINGS}")
n_actions = len(SETTINGS["actions"])
n_episodes = SETTINGS["num_episodes"]
max_episode_len = SETTINGS["max_episode_length"]
dims = SETTINGS["world_dims"]
eps = SETTINGS["eps"]
policy_net = DQN(dims[0], dims[1], dims[2], n_actions).to(DEVICE)
target_net = DQN(dims[0], dims[1], dims[2], n_actions).to(DEVICE)
target_net.load_state_dict(policy_net.state_dict())
target_net.eval()
optimizer = optim.RMSprop(policy_net.parameters())
memory = ExperienceReplay(100)
total_steps = 0
env = gameEnv(partial=False, size=SETTINGS["world_size"])
#
# Methods
# ------------------------------------------------------------------------------
def select_action(state, eps, n_actions):
f"""
Chooses an action either randomly or from the policy.
"""
class DQNAgent:
"""
初始化
@:param env_id : gym环境id
"""
def __init__(self, env_id, config):
# gym
self._env_id = env_id
self._env = gym.make(env_id)
self._state_size = self._env.observation_space.shape[0]
self._action_size = self._env.action_space.n
# 参数
self._gamma = config.gamma
self._learning_rate = config.lr
self._reward_boundary = config.reward_boundary
self._device = torch.device("cuda" if config.cuda and torch.cuda.is_available() else "cpu")
# model
self._model = DQN(self._state_size, self._action_size).to(self._device)
self._optimizer = torch.optim.Adam(self._model.parameters(), lr=self._learning_rate)
# 经验池
self._replay_buffer = deque(maxlen=config.buffer_size)
self._mini_batch = config.mini_batch
# epsilon
self._epsilon = config.epsilon
self._epsilon_min = config.epsilon_min
self._epsilon_decay = config.epsilon_decay
"""
将observation放入双向队列中，队列满时自动删除最旧的元素
"""
def remember(self, state, action, next_state, reward, done):
self._replay_buffer.append((state, action, next_state, reward, done))
# epsilon幂指数下降
if len(self._replay_buffer) > self._mini_batch:
if self._epsilon > self._epsilon_min:
self._epsilon *= self._epsilon_decay
pass
"""
epsilon-greedy action
"""
def act(self, state):
# 类似模拟退火，random返回[0,1]
if np.random.random() <= self._epsilon:
return random.randrange(self._action_size)
else:
# numpy转成tensor，unsqueeze在下标0处新增一个维度
state = torch.tensor(state, dtype=torch.float).unsqueeze(0).to(self._device)
# 模型预测
predict = self._model(state)
# max在第1维处取最大，[1]为下标，[0]为值， [512*2]-> [521]
return predict.max(1)[1].item()
pass
"""
训练
1、从双向队列中采样mini_batch
2、预测next_state
3、更新优化器
"""
def replay(self):
if len(self._replay_buffer) < self._mini_batch:
return
# 1、从双向队列中采样mini_batch
mini_batch = random.sample(self._replay_buffer, self._mini_batch)
# 载入方式一
# state = np.zeros((self._mini_batch, self._state_size))
# next_state = np.zeros((self._mini_batch, self._state_size))
# action, reward, done = [], [], []
#
# for i in range(self._mini_batch):
# state[i] = mini_batch[i][0]
# action.append(mini_batch[i][1])
# next_state[i] = mini_batch[i][2]
# reward.append(mini_batch[i][3])
# done.append(mini_batch[i][4])
# 载入方式二
state, action, next_state, reward, done = zip(*mini_batch)
state = torch.tensor(state, dtype=torch.float).to(self._device)
action = torch.tensor(action, dtype=torch.long).to(self._device)
next_state = torch.tensor(next_state, dtype=torch.float).to(self._device)
reward = torch.tensor(reward, dtype=torch.float).to(self._device)
done = torch.tensor(done, dtype=torch.float).to(self._device)
# 2、预测next_state
q_target = reward + \
self._gamma * self._model(next_state).to(self._device).max(1)[0] * (1 - done)
q_values = self._model(state).to(self._device).gather(1, action.unsqueeze(1)).squeeze(1)
loss_func = nn.MSELoss()
loss = loss_func(q_values, q_target)
# loss = (q_values - q_target.detach()).pow(2).mean()
# 3、更新优化器
self._optimizer.zero_grad()
loss.backward()
self._optimizer.step()
return loss.item()
"""
1、渲染gym环境开始交互
2、训练模型
"""
def training(self):
writer = SummaryWriter(comment="-train-" + self._env_id)
print(self._model)
# 参数
frame_index = 0
episode_index = 1
best_mean_reward = None
mean_reward = 0
total_rewards = []
while mean_reward < self._reward_boundary:
state = self._env.reset()
# 一轮结束，reward置零
episode_reward = 0
while True:
# 1、渲染gym环境开始交互
self._env.render()
# 选择action进行交互
action = self.act(state)
next_state, reward, done, _ = self._env.step(action)
self.remember(state, action, next_state, reward, done)
state = next_state
frame_index += 1
episode_reward += reward
# 2、训练模型
loss = self.replay()
# 游戏结束，开始训练模型
if done:
if loss is not None:
print("episode: %4d, frames: %5d, reward: %5f, loss: %4f, epsilon: %4f" % (
episode_index, frame_index, np.mean(total_rewards[-10:]), loss, self._epsilon))
episode_index += 1
total_rewards.append(episode_reward)
mean_reward = np.mean(total_rewards[-10:])
writer.add_scalar("epsilon", self._epsilon, frame_index)
writer.add_scalar("episode_reward", episode_reward, frame_index)
writer.add_scalar("mean_reward", mean_reward, frame_index)
if best_mean_reward is None or best_mean_reward < mean_reward:
torch.save(self._model.state_dict(), "training-best.dat")
break
self._env.close()
pass
def test(self, model_path):
if model_path is None:
return
self._model.load_state_dict(torch.load(model_path))
self._model.eval()
total_rewards = []
for episode_index in range(10):
episode_reward = 0
done = False
state = self._env.reset()
while not done:
action = self.act(state)
next_state, reward, done, _ = self._env.step(action)
state = next_state
episode_reward += reward
total_rewards.append(episode_reward)
print("episode: %4d, reward: %5f" % (episode_index, np.mean(total_rewards[-10:])))
class DQNAgent:
def __init__(self, state_size, action_size, config=RLConfig()):
self.seed = random.seed(config.seed)
self.state_size = state_size
self.action_size = action_size
self.batch_size = config.batch_size
self.batch_indices = torch.arange(config.batch_size).long().to(device)
self.samples_before_learning = config.samples_before_learning
self.learn_interval = config.learning_interval
self.parameter_update_interval = config.parameter_update_interval
self.per_epsilon = config.per_epsilon
self.tau = config.tau
self.gamma = config.gamma
if config.useDuelingDQN:
self.qnetwork_local = DuelingDQN(state_size, action_size,
config.seed).to(device)
self.qnetwork_target = DuelingDQN(state_size, action_size,
config.seed).to(device)
else:
self.qnetwork_local = DQN(state_size, action_size,
config.seed).to(device)
self.qnetwork_target = DQN(state_size, action_size,
config.seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=config.learning_rate)
self.doubleDQN = config.useDoubleDQN
self.usePER = config.usePER
if self.usePER:
self.memory = PrioritizedReplayBuffer(config.buffer_size,
config.per_alpha)
else:
self.memory = ReplayBuffer(config.buffer_size)
self.t_step = 0
def act(self, state, eps=0.):
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()
if random.random() < eps:
return random.choice(np.arange(self.action_size))
else:
return np.argmax(action_values.cpu().data.numpy())
def step(self, state, action, reward, next_state, done, beta):
self.memory.add(state, action, reward, next_state, done)
self.t_step += 1
if self.t_step % self.learn_interval == 0:
if len(self.memory) > self.samples_before_learning:
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
next_state = torch.from_numpy(next_state).float().unsqueeze(
0).to(device)
target = self.qnetwork_local(state).data
old_val = target[0][action]
target_val = self.qnetwork_target(next_state).data
if done:
target[0][action] = reward
else:
target[0][
action] = reward + self.gamma * torch.max(target_val)
if self.usePER:
states, actions, rewards, next_states, dones, weights, indices = self.memory.sample(
self.batch_size, beta)
else:
indices = None
weights = None
states, actions, rewards, next_states, dones = self.memory.sample(
self.batch_size)
self.learn(states, actions, rewards, next_states, dones,
indices, weights, self.gamma)
def learn(self, states, actions, rewards, next_states, dones, indices,
weights, gamma):
states = torch.from_numpy(np.vstack(states)).float().to(device)
actions = torch.from_numpy(np.vstack(actions)).long().to(device)
rewards = torch.from_numpy(np.vstack(rewards)).float().to(device)
next_states = torch.from_numpy(
np.vstack(next_states)).float().to(device)
dones = torch.from_numpy(np.vstack(dones.astype(
np.uint8))).float().to(device)
Q_targets_next = self.qnetwork_target(next_states).detach()
if self.doubleDQN:
# choose the best action from the local network
next_actions = self.qnetwork_local(next_states).argmax(dim=-1)
Q_targets_next = Q_targets_next[self.batch_indices, next_actions]
else:
Q_targets_next = Q_targets_next.max(1)[0]
Q_targets = rewards + gamma * Q_targets_next.reshape(
(self.batch_size, 1)) * (1 - dones)
pred = self.qnetwork_local(states)
Q_expected = pred.gather(1, actions)
if self.usePER:
errors = torch.abs(Q_expected -
Q_targets).data.numpy() + self.per_epsilon
self.memory.update_priorities(indices, errors)
self.optimizer.zero_grad()
loss = F.mse_loss(Q_expected, Q_targets)
loss.backward()
self.optimizer.step()
if self.t_step % self.parameter_update_interval == 0:
self.soft_update(self.qnetwork_local, self.qnetwork_target,
self.tau)
def soft_update(self, qnetwork_local, qnetwork_target, tau):
for local_param, target_param in zip(qnetwork_local.parameters(),
qnetwork_target.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
import gym
import gym_ple
from argparse import Namespace
from torch import optim
from solver import DQNSolver
from model import mse_loss, reward_positive_func, DQN
config = {'env': 'FlappyBird-v0', 'lr': 1e-5, 'gamma': 0.99, 'seed': 1, 'buffer_size': 50000,
'init_eps': 0.1, 'final_eps': 0.02, 'eps_step': 1000000, 'observate_time': 1000,
'batch_size': 32, 'save_dir': 'results/dqn', 'update_target': 500, 'use_cuda': True,
'display_interval': 10, 'save_interval': 1000, 'episode': 150000,
'visdom': True, 'use_double': False, 'reward_len': 10,
'in_c': 4, 'num_actions': 2}
config = Namespace(**config)
env = gym.make(config.env)
criterion = mse_loss
reward_func = reward_positive_func
model = DQN(in_c=config.in_c, num_actions=config.num_actions)
optimizer = optim.Adam(model.parameters(), lr=config.lr)
solver = DQNSolver(env, model, optimizer, criterion, reward_func, config)
solver.train_q_learning()
return loss
def update_target_networks(self):
self.learner_target_network.load_state_dict(self.learner_network.state_dict())
print("leaner_target_network updated")
ray.init()
policy_net = DQN(19).cuda()
target_net = DQN(19).cuda()
target_net.load_state_dict(policy_net.state_dict())
memory = Memory.remote(50000)
demos = Memory.remote(25000)
optimizer = optim.Adam(policy_net.parameters(), lr=learning_rate, weight_decay=1e-5)
# Copy network params from pretrained Agent
model_path = './dqn_model/pre_trained6.pth'
policy_net.load_state_dict(torch.load(model_path, map_location='cuda:0'))
target_net.load_state_dict(policy_net.state_dict())
#parse_demo2.remote("MineRLTreechop-v0", demos, policy_net.cpu(), target_net.cpu(), optimizer, threshold=60, num_epochs=1, batch_size=4, seq_len=60, gamma=0.99, model_name='pre_trained4.pth')
# learner network initialzation
batch_size = 256
demo_prob = 0.5
learner = Learner.remote(policy_net, batch_size)
# actor network, environments initialization
# Generating each own instances
def main():
env = gym.make(config.ENV_NAME)
agent = DQN(env)
optimizer = optim.Adam(agent.parameters(), lr=0.001)
finished = False
for epoch in range(config.EPOCHS):
state = env.reset()
for step in range(config.ITERATIONS):
action = agent.get_action(state, 'egreedy')
next_state, reward, done, _ = env.step(action[0, 0])
if done:
reward = -1
agent.replay_memory.push(Transition(
config.FloatTensor([state]),
action,
config.FloatTensor([reward]),
config.FloatTensor([next_state]) if not done else None))
state = next_state
if len(agent.replay_memory) >= config.BATCH_SIZE:
batch = agent.replay_memory.sample(config.BATCH_SIZE)
batch = Transition(*zip(*batch))
non_final_mask = config.ByteTensor(
[s is not None for s in batch.next_state])
non_final_next_state_batch = Variable(torch.cat([
s for s in batch.next_state if s is not None]))
state_batch = Variable(torch.cat(batch.state),
requires_grad=False)
action_batch = Variable(torch.cat(batch.action).view(-1, 1),
requires_grad=False)
reward_batch = Variable(torch.cat(batch.reward),
requires_grad=False)
q_values = agent(state_batch).gather(1, action_batch)
s_values = Variable(torch.zeros(config.BATCH_SIZE).type(
config.FloatTensor), requires_grad=False)
s_values[non_final_mask] = agent(
non_final_next_state_batch).max(1)[0]
expected_q_values = config.GAMMA * s_values + reward_batch
loss = F.smooth_l1_loss(torch.sum(q_values),
torch.sum(expected_q_values))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if done:
break
agent.epsilon = config.EPSILON_START - epoch / config.EPOCHS * (
config.EPSILON_START - config.EPSILON_END)
if epoch % config.TEST_INTERVAL == 0:
sum_reward = 0
for _epoch in range(config.TEST_EPOCHS):
epoch_reward = 0
state = env.reset()
for step in range(config.TEST_ITERATIONS):
# env.render()
action = agent.get_action(state) # Default
state, reward, done, _ = env.step(action[0, 0])
if done:
break
epoch_reward += reward
sum_reward += epoch_reward
avg_reward = sum_reward / config.TEST_EPOCHS
print('Epoch: {}, Average Reward: {}'.format(epoch, avg_reward))
print('Current Epsilon:', agent.epsilon)
if avg_reward > 195:
finished = True
if finished:
break
while True:
state = env.reset()
round_reward = 0
for step in range(config.TEST_ITERATIONS):
env.render()
action = agent.get_action(state) # Default
state, reward, done, _ = env.step(action[0, 0])
if done:
break
round_reward += reward
print('Round reward:', round_reward)
class Agent(object):
def __init__(self, args, action_space):
self.action_space = action_space
self.batch_size = args.batch_size
self.discount = args.discount
self.online_net = DQN(args, self.action_space).to(device=args.device)
self.online_net.train()
self.target_net = DQN(args, self.action_space).to(device=args.device)
self.update_target_net()
self.target_net.train()
for param in self.target_net.parameters():
param.requires_grad = False
self.optimiser = optim.Adam(self.online_net.parameters(),
lr=args.lr,
eps=args.adam_eps)
self.loss_func = nn.MSELoss()
# Acts based on single state (no batch)
def act(self, state):
with torch.no_grad():
return self.online_net([state]).argmax(1).item()
# Acts with an ε-greedy policy (used for evaluation only)
def act_e_greedy(
self,
state,
epsilon=0.05): # High ε can reduce evaluation scores drastically
return random.randrange(
self.action_space) if random.random() < epsilon else self.act(
state)
def learn(self, mem):
# Sample transitions
states, actions, next_states, rewards = mem.sample(self.batch_size)
q_eval = self.online_net(states).gather(
1, actions.unsqueeze(1)).squeeze()
with torch.no_grad():
q_eval_next_a = self.online_net(next_states).argmax(1)
q_next = self.target_net(next_states)
q_target = rewards + self.discount * q_next.gather(
1, q_eval_next_a.unsqueeze(1)).squeeze()
loss = self.loss_func(q_eval, q_target)
self.online_net.zero_grad()
loss.backward()
self.optimiser.step()
def update_target_net(self):
self.target_net.load_state_dict(self.online_net.state_dict())
# Save model parameters on current device (don't move model between devices)
def save(self, path):
torch.save(self.online_net.state_dict(), path + '.pth')
# Evaluates Q-value based on single state (no batch)
def evaluate_q(self, state):
with torch.no_grad():
return (self.online_net([state])).max(1)[0].item()
def train(self):
self.online_net.train()
def eval(self):
self.online_net.eval()
class Agent():
def __init__(self, action_size):
self.action_size = action_size
# These are hyper parameters for the DQN
self.discount_factor = 0.99
self.epsilon = 1.0
self.epsilon_min = 0.01
self.explore_step = 500000
self.epsilon_decay = (self.epsilon -
self.epsilon_min) / self.explore_step
self.train_start = 100000
self.update_target = 1000
# Generate the memory
self.memory = ReplayMemory()
# Create the policy net and the target net
self.policy_net = DQN(action_size)
self.policy_net.to(device)
self.target_net = DQN(action_size)
self.target_net.to(device)
self.optimizer = optim.Adam(params=self.policy_net.parameters(),
lr=learning_rate)
self.scheduler = optim.lr_scheduler.StepLR(
self.optimizer,
step_size=scheduler_step_size,
gamma=scheduler_gamma)
# Initialize a target network and initialize the target network to the policy net
### CODE ###
self.update_target_net()
def load_policy_net(self, path):
self.policy_net = torch.load(path)
# after some time interval update the target net to be same with policy net
def update_target_net(self):
### CODE ###
self.target_net.load_state_dict(self.policy_net.state_dict())
"""Get action using policy net using epsilon-greedy policy"""
def get_action(self, state):
if np.random.rand() <= self.epsilon:
### CODE #### (copy over from agent.py!)
return torch.tensor([[random.randrange(self.action_size)]],
device=device,
dtype=torch.long)
else:
### CODE #### (copy over from agent.py!)
with torch.no_grad():
state = torch.FloatTensor(state).unsqueeze(0).cuda()
return self.policy_net(state).max(1)[1].view(1, 1)
# pick samples randomly from replay memory (with batch_size)
def train_policy_net(self, frame):
if self.epsilon > self.epsilon_min:
self.epsilon -= self.epsilon_decay
mini_batch = self.memory.sample_mini_batch(frame)
mini_batch = np.array(mini_batch).transpose()
history = np.stack(mini_batch[0], axis=0)
states = np.float32(history[:, :4, :, :]) / 255.
states = torch.from_numpy(states).cuda()
actions = list(mini_batch[1])
actions = torch.LongTensor(actions).cuda()
rewards = list(mini_batch[2])
rewards = torch.FloatTensor(rewards).cuda()
next_states = np.float32(history[:, 1:, :, :]) / 255.
next_states = torch.tensor(next_states).cuda()
dones = mini_batch[3] # checks if the game is over
musk = torch.tensor(list(map(int, dones == False)), dtype=torch.bool)
# Your agent.py code here with double DQN modifications
### CODE ###
# Compute Q(s_t, a), the Q-value of the current state
### CODE ####
state_action_values = self.policy_net(states).gather(
1, actions.view(batch_size, -1))
# Compute Q function of next state
### CODE ####
next_state_values = torch.zeros(batch_size, device=device).cuda()
non_final_mask = torch.tensor(tuple(
map(lambda s: s is not None, next_states)),
device=device,
dtype=torch.uint8)
non_final_next_states = torch.cat([
i for i in next_states if i is not None
]).view(states.size()).cuda()
# Compute the expected Q values
next_state_values[non_final_mask] = self.target_net(
non_final_next_states).max(1)[0].detach()
expected_state_action_values = (next_state_values *
self.discount_factor) + rewards
# Compute the Huber Loss
### CODE ####
loss = F.smooth_l1_loss(state_action_values.view(32),
expected_state_action_values)
# Optimize the model, .step() both the optimizer and the scheduler!
### CODE ####
self.optimizer.zero_grad()
loss.backward()
for param in self.policy_net.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
class Agent:
def __init__(self,
game: str,
replay_buffer_capacity: int,
replay_start_size: int,
batch_size: int,
discount_factor: float,
lr: float,
device: str = 'cuda:0',
env_seed: int = 0,
frame_buffer_size: int = 4,
print_self=True):
self.device = device
self.discount_factor = discount_factor
self.game = game
self.batch_size = batch_size
self.replay_buf = ReplayBuffer(capacity=replay_buffer_capacity)
self.env = FrameStack(
AtariPreprocessing(
gym.make(self.game),
# noop_max=0,
# terminal_on_life_loss=True,
scale_obs=False),
num_stack=frame_buffer_size)
self.env.seed(env_seed)
self.reset()
self.n_action = self.env.action_space.n
self.policy_net = DQN(self.n_action).to(self.device)
self.target_net = DQN(self.n_action).to(self.device).eval()
self.optimizer = RMSprop(
self.policy_net.parameters(),
alpha=0.95,
# momentum=0.95,
eps=0.01)
if print_self:
print(self)
self._fill_replay_buf(replay_start_size)
def __repr__(self):
return '\n'.join([
'Agent:', f'Game: {self.game}', f'Device: {self.device}',
f'Policy net: {self.policy_net}', f'Target net: {self.target_net}',
f'Replay buf: {self.replay_buf}'
])
def _fill_replay_buf(self, replay_start_size):
for _ in trange(replay_start_size,
desc='Fill replay_buf randomly',
leave=True):
self.step(1.0)
def reset(self):
"""Reset the end, pre-populate self.frame_buf and self.state"""
self.state = self.env.reset()
@torch.no_grad()
def step(self, epsilon, clip_reward=True):
"""
Choose an action based on current state and epsilon-greedy policy
"""
# Choose action
if random.random() <= epsilon:
q_values = None
action = self.env.action_space.sample()
else:
torch_state = torch.tensor(self.state,
dtype=torch.float32,
device=self.device).unsqueeze(0) / 255.0
q_values = self.policy_net(torch_state)
action = int(q_values.argmax(dim=1).item())
# Apply action
next_state, reward, done, _ = self.env.step(action)
if clip_reward:
reward = max(-1.0, min(reward, 1.0))
# Store into replay buffer
self.replay_buf.append(
(torch.tensor(
np.array(self.state), dtype=torch.float32, device="cpu") /
255., action, reward,
torch.tensor(
np.array(next_state), dtype=torch.float32, device="cpu") /
255., done))
# Advance to next state
self.state = next_state
if done:
self.reset()
return reward, q_values, done
def q_update(self):
self.optimizer.zero_grad()
states, actions, rewards, next_states, dones = [
x.to(self.device) for x in self.replay_buf.sample(self.batch_size)
]
with torch.no_grad():
y = torch.where(
dones, rewards, rewards +
self.discount_factor * self.target_net(next_states).max(1)[0])
predicted_values = self.policy_net(states).gather(
1, actions.unsqueeze(-1)).squeeze(-1)
loss = huber(y, predicted_values, 2.)
loss.backward()
self.optimizer.step()
return (y - predicted_values).abs().mean()
class Agent():
def __init__(self, args, env):
self.action_space = env.action_space()
self.atoms = args.atoms
self.Vmin = args.V_min
self.Vmax = args.V_max
self.support = torch.linspace(args.V_min, args.V_max, self.atoms).to(
device=args.device) # Support (range) of z
self.delta_z = (args.V_max - args.V_min) / (self.atoms - 1)
self.batch_size = args.batch_size
self.n = args.multi_step
self.discount = args.discount
self.norm_clip = args.norm_clip
self.online_net = DQN(args, self.action_space).to(device=args.device)
if args.model: # Load pretrained model if provided
if os.path.isfile(args.model):
state_dict = torch.load(
args.model, map_location='cpu'
) # Always load tensors onto CPU by default, will shift to GPU if necessary
if 'conv1.weight' in state_dict.keys():
for old_key, new_key in (('conv1.weight',
'convs.0.weight'),
('conv1.bias', 'convs.0.bias'),
('conv2.weight',
'convs.2.weight'),
('conv2.bias', 'convs.2.bias'),
('conv3.weight',
'convs.4.weight'),
('conv3.bias', 'convs.4.bias')):
state_dict[new_key] = state_dict[
old_key] # Re-map state dict for old pretrained models
del state_dict[
old_key] # Delete old keys for strict load_state_dict
self.online_net.load_state_dict(state_dict)
print("Loading pretrained model: " + args.model)
else: # Raise error if incorrect model path provided
raise FileNotFoundError(args.model)
self.online_net.train()
self.target_net = DQN(args, self.action_space).to(device=args.device)
self.update_target_net()
self.target_net.train()
for param in self.target_net.parameters():
param.requires_grad = False
# self.optimiser = optim.Adam(self.online_net.parameters(), lr=args.learning_rate, eps=args.adam_eps)
self.convs_optimiser = optim.Adam(self.online_net.convs.parameters(),
lr=args.learning_rate,
eps=args.adam_eps)
self.linear_optimiser = optim.Adam(chain(
self.online_net.fc_h_v.parameters(),
self.online_net.fc_h_a.parameters(),
self.online_net.fc_z_v.parameters(),
self.online_net.fc_z_a.parameters()),
lr=args.learning_rate,
eps=args.adam_eps)
# Resets noisy weights in all linear layers (of online net only)
def reset_noise(self):
self.online_net.reset_noise()
# Acts based on single state (no batch)
def act(self, state):
with torch.no_grad():
# don't count these calls since it is accounted for after "action = dqn.act(state)" in main.py
ret = (self.online_net(state.unsqueeze(0)) *
self.support).sum(2).argmax(1).item()
return ret
# Acts with an ε-greedy policy (used for evaluation only)
def act_e_greedy(
self,
state,
epsilon=0.001): # High ε can reduce evaluation scores drastically
return np.random.randint(
0, self.action_space
) if np.random.random() < epsilon else self.act(state)
def learn(self, mem, freeze=False):
# Sample transitions
idxs, states, actions, returns, next_states, nonterminals, weights, _ = mem.sample(
self.batch_size)
# Calculate current state probabilities (online network noise already sampled)
log_ps = self.online_net(
states, log=True) # Log probabilities log p(s_t, ·; θonline)
log_ps_a = log_ps[range(self.batch_size),
actions] # log p(s_t, a_t; θonline)
with torch.no_grad():
# Calculate nth next state probabilities
pns = self.online_net(
next_states) # Probabilities p(s_t+n, ·; θonline)
dns = self.support.expand_as(
pns) * pns # Distribution d_t+n = (z, p(s_t+n, ·; θonline))
argmax_indices_ns = dns.sum(2).argmax(
1
) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))]
self.target_net.reset_noise() # Sample new target net noise
pns = self.target_net(
next_states) # Probabilities p(s_t+n, ·; θtarget)
pns_a = pns[range(
self.batch_size
), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget)
# Compute Tz (Bellman operator T applied to z)
Tz = returns.unsqueeze(1) + nonterminals * (
self.discount**self.n) * self.support.unsqueeze(
0) # Tz = R^n + (γ^n)z (accounting for terminal states)
Tz = Tz.clamp(min=self.Vmin,
max=self.Vmax) # Clamp between supported values
# Compute L2 projection of Tz onto fixed support z
b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz
l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64)
# Fix disappearing probability mass when l = b = u (b is int)
l[(u > 0) * (l == u)] -= 1
u[(l < (self.atoms - 1)) * (l == u)] += 1
# Distribute probability of Tz
m = states.new_zeros(self.batch_size, self.atoms)
offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms),
self.batch_size).unsqueeze(1).expand(
self.batch_size,
self.atoms).to(actions)
m.view(-1).index_add_(
0, (l + offset).view(-1),
(pns_a *
(u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b)
m.view(-1).index_add_(
0, (u + offset).view(-1),
(pns_a *
(b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l)
loss = -torch.sum(
m * log_ps_a,
1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t)))
self.online_net.zero_grad()
loss.mean().backward(
) # Backpropagate importance-weighted minibatch loss
clip_grad_norm_(self.online_net.parameters(),
self.norm_clip) # Clip gradients by L2 norm
# self.optimiser.step()
if not freeze:
self.convs_optimiser.step()
self.linear_optimiser.step()
def learn_with_latent(self, latent_mem):
# Sample transitions
idxs, states, actions, returns, next_states, nonterminals, weights, ns = latent_mem.sample(
self.batch_size)
# Calculate current state probabilities (online network noise already sampled)
log_ps = self.online_net.forward_with_latent(
states, log=True) # Log probabilities log p(s_t, ·; θonline)
log_ps_a = log_ps[range(self.batch_size),
actions] # log p(s_t, a_t; θonline)
with torch.no_grad():
# Calculate nth next state probabilities
pns = self.online_net.forward_with_latent(
next_states) # Probabilities p(s_t+n, ·; θonline)
dns = self.support.expand_as(
pns) * pns # Distribution ds_t+n = (z, p(s_t+n, ·; θonline))
argmax_indices_ns = dns.sum(2).argmax(
1
) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))]
self.target_net.reset_noise() # Sample new target net noise
pns = self.target_net.forward_with_latent(
next_states) # Probabilities p(s_t+n, ·; θtarget)
pns_a = pns[range(
self.batch_size
), argmax_indices_ns] # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget)
# use ns instead of self.n since n is possibly different for each sequence in the batch
ns = torch.tensor(ns, device=latent_mem.device).unsqueeze(1)
# Compute Tz (Bellman operator T applied to z)
Tz = returns.unsqueeze(1) + nonterminals * (
self.discount**ns) * self.support.unsqueeze(
0) # Tz = R^n + (γ^n)z (accounting for terminal states)
Tz = Tz.clamp(min=self.Vmin,
max=self.Vmax) # Clamp between supported values
# Compute L2 projection of Tz onto fixed support z
b = (Tz - self.Vmin) / self.delta_z # b = (Tz - Vmin) / Δz
l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64)
# Fix disappearing probability mass when l = b = u (b is int)
l[(u > 0) * (l == u)] -= 1
u[(l < (self.atoms - 1)) * (l == u)] += 1
# Distribute probability of Tz
m = states.new_zeros(self.batch_size, self.atoms)
offset = torch.linspace(0, ((self.batch_size - 1) * self.atoms),
self.batch_size).unsqueeze(1).expand(
self.batch_size,
self.atoms).to(actions)
m.view(-1).index_add_(
0, (l + offset).view(-1),
(pns_a *
(u.float() - b)).view(-1)) # m_l = m_l + p(s_t+n, a*)(u - b)
m.view(-1).index_add_(
0, (u + offset).view(-1),
(pns_a *
(b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l)
loss = -torch.sum(
m * log_ps_a,
1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t)))
self.online_net.zero_grad()
loss.mean().backward(
) # Backpropagate importance-weighted minibatch loss
clip_grad_norm_(self.online_net.parameters(),
self.norm_clip) # Clip gradients by L2 norm
# self.optimiser.step()
self.linear_optimiser.step()
def update_target_net(self):
self.target_net.load_state_dict(self.online_net.state_dict())
# Save model parameters on current device (don't move model between devices)
def save(self, path, name='model.pth'):
torch.save(self.online_net.state_dict(), os.path.join(path, name))
# Evaluates Q-value based on single state (no batch)
def evaluate_q(self, state):
with torch.no_grad():
return (self.online_net(state.unsqueeze(0)) *
self.support).sum(2).max(1)[0].item()
def train(self):
self.online_net.train()
def eval(self):
self.online_net.eval()
class DQNAgent:
def __init__(self, config: Config):
self.config = config
self.is_training = True
self.buffer = ReplayBuffer(self.config.max_buff)
self.model = DQN(self.config.state_dim, self.config.action_dim).cuda()
self.model_optim = Adam(self.model.parameters(), lr=self.config.learning_rate)
if self.config.use_cuda:
self.cuda()
def act(self, state, epsilon=None):
if epsilon is None: epsilon = self.config.epsilon_min
if random.random() > epsilon or not self.is_training:
state = torch.tensor(state, dtype=torch.float).unsqueeze(0)
if self.config.use_cuda:
state = state.cuda()
q_value = self.model.forward(state)
action = q_value.max(1)[1].item()
else:
action = random.randrange(self.config.action_dim)
return action
def learning(self, fr):
s0, a, r, s1, done = self.buffer.sample(self.config.batch_size)
s0 = torch.tensor(s0, dtype=torch.float)
s1 = torch.tensor(s1, dtype=torch.float)
a = torch.tensor(a, dtype=torch.long)
r = torch.tensor(r, dtype=torch.float)
done = torch.tensor(done, dtype=torch.float)
if self.config.use_cuda:
s0 = s0.cuda()
s1 = s1.cuda()
a = a.cuda()
r = r.cuda()
done = done.cuda()
q_values = self.model(s0).cuda()
next_q_values = self.model(s1).cuda()
next_q_value = next_q_values.max(1)[0]
q_value = q_values.gather(1, a.unsqueeze(1)).squeeze(1)
expected_q_value = r + self.config.gamma * next_q_value * (1 - done)
# Notice that detach the expected_q_value
loss = (q_value - expected_q_value.detach()).pow(2).mean()
self.model_optim.zero_grad()
loss.backward()
self.model_optim.step()
return loss.item()
def cuda(self):
self.model.cuda()
def load_weights(self, model_path):
if model_path is None: return
self.model.load_state_dict(torch.load(model_path))
def save_model(self, output, tag=''):
torch.save(self.model.state_dict(), '%s/model_%s.pkl' % (output, tag))
def save_config(self, output):
with open(output + '/config.txt', 'w') as f:
attr_val = get_class_attr_val(self.config)
for k, v in attr_val.items():
f.write(str(k) + " = " + str(v) + "\n")
class DQNAgent:
def __init__(self, env, render, config_info):
self.env = env
self._reset_env()
self.render = render
# Set seeds
self.seed = 0
env.seed(self.seed)
torch.manual_seed(self.seed)
np.random.seed(self.seed)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Device in use : {self.device}")
# Define checkpoint
checkpoint = Checkpoint(self.device, **config_info)
# Create / load checkpoint dict
(
self.ckpt,
self.path_ckpt_dict,
self.path_ckpt,
config,
) = checkpoint.manage_checkpoint()
# Unroll useful parameters from config dict
self.batch_size = config["training"]["batch_size"]
self.max_timesteps = config["training"]["max_timesteps"]
self.replay_size = config["training"]["replay_size"]
self.start_temp = config["training"]["start_temperature"]
self.final_temp = config["training"]["final_temperature"]
self.decay_temp = config["training"]["decay_temperature"]
self.gamma = config["training"]["gamma"]
self.early_stopping = config["training"]["early_stopping"]
self.update_frequency = config["training"]["update_frequency"]
self.eval_frequency = config["training"]["eval_frequency"]
# Define state and action dimension spaces
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
# Define Q-network and target Q-network
self.network = DQN(state_dim, action_dim, **config["model"]).to(self.device)
self.target_network = DQN(state_dim, action_dim, **config["model"]).to(
self.device
)
# Loss and optimizer
self.criterion = nn.MSELoss()
lr = config["optimizer"]["learning_rate"]
self.optimizer = optim.Adam(self.network.parameters(), lr=lr)
# Load network's weight if resume training
checkpoint.load_weights(
self.ckpt, self.network, self.target_network, self.optimizer
)
# Initialize replay buffer
self.replay_buffer = ReplayBuffer(self.replay_size)
self.transition = namedtuple(
"transition",
field_names=["state", "action", "reward", "done", "next_state"],
)
def _reset_env(self):
self.state, self.done = self.env.reset(), False
self.episode_reward = 0.0
def play_step(self, temperature=1):
reward_signal = None
# Boltmann exploration
state_v = torch.tensor(self.state, dtype=torch.float32).to(self.device)
q_values = self.network(state_v)
probas = Categorical(F.softmax(q_values / temperature, dim=0))
action = probas.sample().item()
# Perform one step in the environment
next_state, reward, self.done, _ = self.env.step(action)
# Create a tuple for the new transition
new_transition = self.transition(
self.state, action, reward, self.done, next_state
)
# Add transition to the replay buffer
self.replay_buffer.store_transition(new_transition)
self.state = next_state
self.episode_reward += reward
if self.render:
self.env.render()
if self.done:
reward_signal = self.episode_reward
self._reset_env()
return reward_signal
def train(self):
# Initializations
all_episode_rewards = []
episode_timestep = 0
best_mean_reward = None
episode_num = 0
temp = self.start_temp # start epsilon to explore while filling the buffer
writer = SummaryWriter(log_dir=self.path_ckpt, comment="-dqn")
# Evaluate untrained policy
evaluations = [self.eval_policy()]
# Training loop
for t in range(int(self.max_timesteps)):
episode_timestep += 1
# -> is None if episode is not terminated
# -> is episode reward when episode is terminated
reward_signal = self.play_step(temp)
# when episode is terminated
if reward_signal is not None:
episode_reward = reward_signal
mean_reward = np.mean(all_episode_rewards[-10:])
print(
f"Timestep [{t + 1}/{int(self.max_timesteps)}] ; "
f"Episode num : {episode_num + 1} ; "
f"Episode length : {episode_timestep} ; "
f"Reward : {episode_reward:.2f} ; "
f"Mean reward {mean_reward:.2f}"
)
# Save episode's reward & reset counters
all_episode_rewards.append(episode_reward)
episode_timestep = 0
episode_num += 1
# Save checkpoint
self.ckpt["episode_num"] = episode_num
self.ckpt["all_episode_rewards"].append(episode_reward)
self.ckpt["optimizer_state_dict"] = self.optimizer.state_dict()
torch.save(self.ckpt, self.path_ckpt_dict)
writer.add_scalar("episode reward", episode_reward, t)
writer.add_scalar("mean reward", mean_reward, t)
# Save network if performance is better than average
if best_mean_reward is None or best_mean_reward < mean_reward:
self.ckpt["best_mean_reward"] = mean_reward
self.ckpt["model_state_dict"] = self.network.state_dict()
self.ckpt[
"target_model_state_dict"
] = self.target_network.state_dict()
if best_mean_reward is not None:
print(f"Best mean reward updated : {best_mean_reward}")
best_mean_reward = mean_reward
# Criterion to early stop training
if mean_reward > self.early_stopping:
self.plot_reward()
print(f"Solved in {t + 1} timesteps!")
break
# Fill the replay buffer
if len(self.replay_buffer) < self.replay_size:
continue
else:
# Adjust exploration parameter
temp = np.maximum(
self.final_temp, self.start_temp - (t / self.decay_temp)
)
writer.add_scalar("temperature", temp, t)
# Get the weights of the network before update
weights_network = self.network.state_dict()
# when it's time perform a batch gradient descent
if t % self.update_frequency == 0:
# Backward and optimize
self.optimizer.zero_grad()
batch = self.replay_buffer.sample_buffer(self.batch_size)
loss = self.train_on_batch(batch)
loss.backward()
self.optimizer.step()
# Synchronize target network
self.target_network.load_state_dict(weights_network)
# Evaluate episode
if (t + 1) % self.eval_frequency == 0:
evaluations.append(self.eval_policy())
np.save(self.path_ckpt, evaluations)
def train_on_batch(self, batch_samples):
# Unpack batch_size of transitions randomly drawn from the replay buffer
states, actions, rewards, dones, next_states = batch_samples
# Transform np arrays into tensors and send them to device
states_v = torch.tensor(states).to(self.device)
next_states_v = torch.tensor(next_states).to(self.device)
actions_v = torch.tensor(actions).to(self.device)
rewards_v = torch.tensor(rewards).to(self.device)
dones_bool = torch.tensor(dones, dtype=torch.bool).to(self.device)
# Vectorized version
q_vals = self.network(states_v) # dim=batch_size x num_actions
# Get the Q-values corresponding to the action
q_vals = q_vals.gather(1, actions_v.view(-1, 1))
q_vals = q_vals.view(1, -1)[0]
target_next_q_vals = self.target_network(next_states_v)
# Max action of the target Q-values
target_max_next_q_vals, _ = torch.max(target_next_q_vals, dim=1)
# If state is terminal
target_max_next_q_vals[dones_bool] = 0.0
# No update of the target during backpropagation
target_max_next_q_vals = target_max_next_q_vals.detach()
# Bellman approximation for target Q-values
target_q_vals = rewards_v + self.gamma * target_max_next_q_vals
return self.criterion(q_vals, target_q_vals)
def eval_policy(self, eval_episodes=10):
# Runs policy for X episodes and returns average reward
# A fixed seed is used for the eval environment
self.env.seed(self.seed + 100)
avg_reward = 0.0
temperature = 1
for _ in range(eval_episodes):
self._reset_env()
reward_signal = None
while reward_signal is None:
reward_signal = self.play_step(temperature)
avg_reward += reward_signal
avg_reward /= eval_episodes
print("---------------------------------------")
print(f"Evaluation over {eval_episodes} episodes: {avg_reward:.3f}")
print("---------------------------------------")
return avg_reward
def plot_reward(self):
plt.plot(self.ckpt["all_episode_rewards"])
plt.xlabel("Episode")
plt.ylabel("Reward")
plt.title(f"Reward evolution for {self.env.unwrapped.spec.id} Gym environment")
plt.tight_layout()
path_fig = os.path.join(self.path_ckpt, "figure.png")
plt.savefig(path_fig)
print(f"Figure saved to {path_fig}")
plt.show()
env = StocksEnv(prices)
env_val = StocksEnv(val_price)
net = DQN(env.observation_space.shape[0], env.action_space.n)
tgt_net = ptan.agent.TargetNet(net)
selector = ptan.actions.EpsilonGreedyActionSelector(
epsilon=params.epsilon_start)
epsilon_tracker = ptan.actions.EpsilonTracker(
selector, params.epsilon_start, params.epsilon_final, params.epsilon_steps)
agent = ptan.agent.DQNAgent(net, selector, device=device)
exp_source = ptan.experience.ExperienceSourceFirstLast(
env, agent, gamma=args.gamma, steps_count=args.reward_steps)
buffer = ptan.experience.ExperienceReplayBuffer(
exp_source, buffer_size=params.replay_size)
optimizer = optim.Adam(net.parameters(), lr=params.learning_rate)
def process_batch(engine, batch):
optimizer.zero_grad()
loss_v = common.calc_loss_dqn(
batch, net, tgt_net.target_model, gamma=params.gamma, device=device)
loss_v.backward()
optimizer.step()
epsilon_tracker.frame(engine.state.iteration)
if engine.state.iteration % params.target_net_sync == 0:
tgt_net.sync()
return {
'loss': loss_v.item(),
'epsilon': selector.epsilon
}
class Agent:
"""
The intelligent agent of the simulation. Set the model of the neural network used and general parameters.
It is responsible to select the actions, optimize the neural network and manage the models.
"""
def __init__(self, action_set, train=True, load_path=None):
#1. Initialize agent params
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.action_set = action_set
self.action_number = len(action_set)
self.steps_done = 0
self.epsilon = Config.EPS_START
self.episode_durations = []
#2. Build networks
self.policy_net = DQN().to(self.device)
self.target_net = DQN().to(self.device)
self.optimizer = optim.RMSprop(self.policy_net.parameters(), lr=Config.LEARNING_RATE)
if not train:
self.optimizer = optim.RMSprop(self.policy_net.parameters(), lr=0)
self.policy_net.load(load_path, optimizer=self.optimizer)
self.policy_net.eval()
self.target_net.load_state_dict(self.policy_net.state_dict())
self.target_net.eval()
#3. Create Prioritized Experience Replay Memory
self.memory = Memory(Config.MEMORY_SIZE)
def append_sample(self, state, action, next_state, reward):
"""
save sample (error,<s,a,s',r>) to the replay memory
"""
# Define if is the end of the simulation
done = True if next_state is None else False
# Compute Q(s_t, a) - the model computes Q(s_t), then we select the columns of actions taken
state_action_values = self.policy_net(state)
state_action_values = state_action_values.gather(1, action.view(-1,1))
if not done:
# Compute argmax Q(s', a; θ)
next_state_actions = self.policy_net(next_state).max(1)[1].detach().unsqueeze(1)
# Compute Q(s', argmax Q(s', a; θ), θ-)
next_state_values = self.target_net(next_state).gather(1, next_state_actions).squeeze(1).detach()
# Compute the expected Q values
expected_state_action_values = (next_state_values * Config.GAMMA) + reward
else:
expected_state_action_values = reward
error = abs(state_action_values - expected_state_action_values).data.cpu().numpy()
self.memory.add(error, state, action, next_state, reward)
def select_action(self, state, train=True):
"""
Selet the best action according to the Q-values outputed from the neural network
Parameters
----------
state: float ndarray
The current state on the simulation
train: bool
Define if we are evaluating or trainning the model
Returns
-------
a.max(1)[1]: int
The action with the highest Q-value
a.max(0): float
The Q-value of the action taken
"""
global steps_done
sample = random.random()
#1. Perform a epsilon-greedy algorithm
#a. set the value for epsilon
self.epsilon = Config.EPS_END + (Config.EPS_START - Config.EPS_END) * \
math.exp(-1. * self.steps_done / Config.EPS_DECAY)
self.steps_done += 1
#b. make the decision for selecting a random action or selecting an action from the neural network
if sample > self.epsilon or (not train):
# select an action from the neural network
with torch.no_grad():
# a <- argmax Q(s, theta)
a = self.policy_net(state)
return a.max(1)[1].view(1, 1), a.max(0)
else:
# select a random action
print('random action')
return torch.tensor([[random.randrange(2)]], device=self.device, dtype=torch.long), None
"""
def select_action(self, state, train=True):
Selet the best action according to the Q-values outputed from the neural network
Parameters
----------
state: float ndarray
The current state on the simulation
train: bool
Define if we are evaluating or trainning the model
Returns
-------
a.max(1)[1]: int
The action with the highest Q-value
a.max(0): float
The Q-value of the action taken
global steps_done
sample = random.random()
#1. Perform a epsilon-greedy algorithm
#a. set the value for epsilon
self.epsilon = Config.EPS_END + (Config.EPS_START - Config.EPS_END) * \
math.exp(-1. * self.steps_done / Config.EPS_DECAY)
self.steps_done += 1
#b. make the decision for selecting a random action or selecting an action from the neural network
if sample > self.epsilon or (not train):
# select an action from the neural network
with torch.no_grad():
# a <- argmax Q(s, theta)
#set the network to train mode is important to enable dropout
self.policy_net.train()
output_list = []
# Retrieve the outputs from neural network feedfoward n times to build a statistic model
for i in range(Config.STOCHASTIC_PASSES):
#print(agent.policy_net(data))
output_list.append(torch.unsqueeze(F.softmax(self.policy_net(state)), 0))
#print(output_list[i])
self.policy_net.eval()
# The result of the network is the mean of n passes
output_mean = torch.cat(output_list, 0).mean(0)
q_value = output_mean.data.cpu().numpy().max()
action = output_mean.max(1)[1].view(1, 1)
uncertainty = torch.cat(output_list, 0).var(0).mean().item()
return action, q_value, uncertainty
else:
# select a random action
print('random action')
return torch.tensor([[random.randrange(2)]], device=self.device, dtype=torch.long), None, None
"""
def optimize_model(self):
"""
Perform one step of optimization on the neural network
"""
if self.memory.tree.n_entries < Config.BATCH_SIZE:
return
transitions, idxs, is_weights = self.memory.sample(Config.BATCH_SIZE)
# Transpose the batch (see http://stackoverflow.com/a/19343/3343043 for detailed explanation).
batch = Transition(*zip(*transitions))
# Compute a mask of non-final states and concatenate the batch elements
non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,
batch.next_state)), device=self.device, dtype=torch.uint8)
non_final_next_states = torch.cat([s for s in batch.next_state
if s is not None])
state_batch = torch.cat(batch.state)
action_batch = torch.cat(batch.action)
reward_batch = torch.cat(batch.reward)
# Compute Q(s_t, a) - the model computes Q(s_t), then we select the columns of actions taken
state_action_values = self.policy_net(state_batch).gather(1, action_batch)
# Compute argmax Q(s', a; θ)
next_state_actions = self.policy_net(non_final_next_states).max(1)[1].detach().unsqueeze(1)
# Compute Q(s', argmax Q(s', a; θ), θ-)
next_state_values = torch.zeros(Config.BATCH_SIZE, device=self.device)
next_state_values[non_final_mask] = self.target_net(non_final_next_states).gather(1, next_state_actions).squeeze(1).detach()
# Compute the expected Q values
expected_state_action_values = (next_state_values * Config.GAMMA) + reward_batch
# Update priorities
errors = torch.abs(state_action_values.squeeze() - expected_state_action_values).data.cpu().numpy()
# update priority
for i in range(Config.BATCH_SIZE):
idx = idxs[i]
self.memory.update(idx, errors[i])
# Compute Huber loss
loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1))
loss_return = loss.item()
# Optimize the model
self.optimizer.zero_grad()
loss.backward()
for param in self.policy_net.parameters():
param.grad.data.clamp_(-1, 1)
self.optimizer.step()
return loss_return
def save(self, step, logs_path, label):
"""
Save the model on hard disc
Parameters
----------
step: int
current step on the simulation
logs_path: string
path to where we will store the model
label: string
label that will be used to store the model
"""
os.makedirs(logs_path + label, exist_ok=True)
full_label = label + str(step) + '.pth'
logs_path = os.path.join(logs_path, label, full_label)
self.policy_net.save(logs_path, step=step, optimizer=self.optimizer)
def restore(self, logs_path):
"""
Load the model from hard disc
Parameters
----------
logs_path: string
path to where we will store the model
"""
self.policy_net.load(logs_path)
self.target_net.load(logs_path)
