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()
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(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, 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 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, 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()
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()
class Agent(object):
""" all improvments from Rainbow research work
"""
def __init__(self, args, state_size, action_size):
"""
Args:
param1 (args): args
param2 (int): args
param3 (int): args
"""
self.action_size = action_size
self.state_size = state_size
self.atoms = args.atoms
self.V_min = args.V_min
self.V_max = args.V_max
self.device = args.device
self.support = torch.linspace(args.V_min, args.V_max, self.atoms).to(
device=self.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.qnetwork_local = DQN(args, self.state_size,
self.action_size).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.qnetwork_local.load_state_dict(
torch.load(args.model, map_location='cpu'))
self.qnetwork_local.train()
self.target_net = DQN(args, self.state_size,
self.action_size).to(device=args.device)
self.update_target_net()
self.target_net.train()
for param in self.target_net.parameters():
param.requires_grad = False
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=args.lr,
eps=args.adam_eps)
def reset_noise(self):
""" resets noisy weights in all linear layers """
self.qnetwork_local.reset_noise()
def act(self, state):
"""
acts greedy(max) based on a single state
Args:
param1 (int) : state
"""
with torch.no_grad():
return (self.qnetwork_local(state.unsqueeze(0).to(self.device)) *
self.support).sum(2).argmax(1).item()
def act_e_greedy(self, state, epsilon=0.001):
""" acts with epsilon greedy policy
epsilon exploration vs exploitation traide off
Args:
param1(int): state
param2(float): epsilon
Return : action int number between 0 and 4
"""
return np.random.randint(
0, self.action_size) if np.random.random() < epsilon else self.act(
state)
def learn(self, mem):
""" uses samples with the given batch size to improve the Q function
Args:
param1 (Experince Replay Buffer) : 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.qnetwork_local(
states, log=True) # Log probabilities log p(s_t, *; theta online)
log_ps_a = log_ps[range(self.batch_size),
actions] # log p(s_t, a_t; theat online)
with torch.no_grad():
# Calculate nth next state probabilities
pns = self.qnetwork_local(
next_states) # Probabilities p(s_t+n, *; theta online)
dns = self.support.expand_as(
pns
) * pns # Distribution d_t+n = (z, p(s_t+n, *; theat online))
argmax_indices_ns = dns.sum(2).argmax(
1
) # Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; theat online))]
self.target_net.reset_noise() # Sample new target net noise
pns = self.target_net(
next_states) # Probabilities p(s_t+n, ; theata target)
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; theat online))]; theat target)
# Compute Tz (Bellman operator T applied to z)
Tz = returns.unsqueeze(1) + nonterminals * (
self.discount**self.n
) * self.support.unsqueeze(
0) # Tz = R^n + (discoit ^n)z (accounting for terminal states)
Tz = Tz.clamp(min=self.V_min,
max=self.V_max) # Clamp between supported values
# Compute L2 projection of Tz onto fixed support z
b = (Tz - self.V_min) / self.delta_z # b = (Tz - Vmin) / delta z
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.qnetwork_local.zero_grad()
(weights * loss).mean().backward(
) # Backpropagate importance-weighted minibatch loss
self.optimizer.step()
mem.update_priorities(idxs,
loss.detach().cpu().numpy()
) # Update priorities of sampled transitions
self.soft_update()
def soft_update(self, tau=1e-3):
""" swaps the network weights from the online to the target
Args:
param1 (float): tau
"""
for target_param, local_param in zip(self.target_net.parameters(),
self.qnetwork_local.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
def update_target_net(self):
""" copy the model weights from the online to the target network """
self.target_net.load_state_dict(self.qnetwork_local.state_dict())
def save(self, path):
""" save the model weights to a file
Args:
param1 (string): pathname
"""
torch.save(self.qnetwork_local.state_dict(),
os.path.join(path, 'model.pth'))
def evaluate_q(self, state):
""" Evaluates Q-value based on single state
"""
with torch.no_grad():
return (self.qnetwork_local(state.unsqueeze(0)) *
self.support).sum(2).max(1)[0].item()
def train(self):
"""
activates the backprob. layers for the online network
"""
self.qnetwork_local.train()
def eval(self):
""" invoke the eval from the online network
deactivates the backprob
layers like dropout will work in eval model instead
"""
self.qnetwork_local.eval()
class Agent:
def __init__(self):
self.mode = "train"
with open("config.yaml") as reader:
self.config = yaml.safe_load(reader)
print(self.config)
self.load_config()
self.online_net = DQN(config=self.config,
word_vocab=self.word_vocab,
char_vocab=self.char_vocab,
answer_type=self.answer_type)
self.target_net = DQN(config=self.config,
word_vocab=self.word_vocab,
char_vocab=self.char_vocab,
answer_type=self.answer_type)
self.online_net.train()
self.target_net.train()
self.update_target_net()
for param in self.target_net.parameters():
param.requires_grad = False
if self.use_cuda:
self.online_net.cuda()
self.target_net.cuda()
self.naozi = ObservationPool(capacity=self.naozi_capacity)
# optimizer
self.optimizer = torch.optim.Adam(
self.online_net.parameters(),
lr=self.config['training']['optimizer']['learning_rate'])
self.clip_grad_norm = self.config['training']['optimizer'][
'clip_grad_norm']
def load_config(self):
# word vocab
with open("vocabularies/word_vocab.txt") as f:
self.word_vocab = f.read().split("\n")
self.word2id = {}
for i, w in enumerate(self.word_vocab):
self.word2id[w] = i
# char vocab
with open("vocabularies/char_vocab.txt") as f:
self.char_vocab = f.read().split("\n")
self.char2id = {}
for i, w in enumerate(self.char_vocab):
self.char2id[w] = i
self.EOS_id = self.word2id["</s>"]
self.train_data_size = self.config['general']['train_data_size']
self.question_type = self.config['general']['question_type']
self.random_map = self.config['general']['random_map']
self.testset_path = self.config['general']['testset_path']
self.naozi_capacity = self.config['general']['naozi_capacity']
self.eval_folder = pjoin(
self.testset_path, self.question_type,
("random_map" if self.random_map else "fixed_map"))
self.eval_data_path = pjoin(self.testset_path, "data.json")
self.batch_size = self.config['training']['batch_size']
self.max_nb_steps_per_episode = self.config['training'][
'max_nb_steps_per_episode']
self.max_episode = self.config['training']['max_episode']
self.target_net_update_frequency = self.config['training'][
'target_net_update_frequency']
self.learn_start_from_this_episode = self.config['training'][
'learn_start_from_this_episode']
self.run_eval = self.config['evaluate']['run_eval']
self.eval_batch_size = self.config['evaluate']['batch_size']
self.eval_max_nb_steps_per_episode = self.config['evaluate'][
'max_nb_steps_per_episode']
# Set the random seed manually for reproducibility.
self.random_seed = self.config['general']['random_seed']
np.random.seed(self.random_seed)
torch.manual_seed(self.random_seed)
if torch.cuda.is_available():
if not self.config['general']['use_cuda']:
print(
"WARNING: CUDA device detected but 'use_cuda: false' found in config.yaml"
)
self.use_cuda = False
else:
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed(self.random_seed)
self.use_cuda = True
else:
self.use_cuda = False
if self.question_type == "location":
self.answer_type = "pointing"
elif self.question_type in ["attribute", "existence"]:
self.answer_type = "2 way"
else:
raise NotImplementedError
self.save_checkpoint = self.config['checkpoint']['save_checkpoint']
self.experiment_tag = self.config['checkpoint']['experiment_tag']
self.save_frequency = self.config['checkpoint']['save_frequency']
self.load_pretrained = self.config['checkpoint']['load_pretrained']
self.load_from_tag = self.config['checkpoint']['load_from_tag']
self.qa_loss_lambda = self.config['training']['qa_loss_lambda']
self.interaction_loss_lambda = self.config['training'][
'interaction_loss_lambda']
# replay buffer and updates
self.discount_gamma = self.config['replay']['discount_gamma']
self.replay_batch_size = self.config['replay']['replay_batch_size']
self.command_generation_replay_memory = command_generation_memory.PrioritizedReplayMemory(
self.config['replay']['replay_memory_capacity'],
priority_fraction=self.config['replay']
['replay_memory_priority_fraction'],
discount_gamma=self.discount_gamma)
self.qa_replay_memory = qa_memory.PrioritizedReplayMemory(
self.config['replay']['replay_memory_capacity'],
priority_fraction=0.0)
self.update_per_k_game_steps = self.config['replay'][
'update_per_k_game_steps']
self.multi_step = self.config['replay']['multi_step']
# distributional RL
self.use_distributional = self.config['distributional']['enable']
self.atoms = self.config['distributional']['atoms']
self.v_min = self.config['distributional']['v_min']
self.v_max = self.config['distributional']['v_max']
self.support = torch.linspace(self.v_min, self.v_max,
self.atoms) # Support (range) of z
if self.use_cuda:
self.support = self.support.cuda()
self.delta_z = (self.v_max - self.v_min) / (self.atoms - 1)
# dueling networks
self.dueling_networks = self.config['dueling_networks']
# double dqn
self.double_dqn = self.config['double_dqn']
# counting reward
self.revisit_counting_lambda_anneal_episodes = self.config[
'episodic_counting_bonus'][
'revisit_counting_lambda_anneal_episodes']
self.revisit_counting_lambda_anneal_from = self.config[
'episodic_counting_bonus']['revisit_counting_lambda_anneal_from']
self.revisit_counting_lambda_anneal_to = self.config[
'episodic_counting_bonus']['revisit_counting_lambda_anneal_to']
self.revisit_counting_lambda = self.revisit_counting_lambda_anneal_from
# valid command bonus
self.valid_command_bonus_lambda = self.config[
'valid_command_bonus_lambda']
# epsilon greedy
self.epsilon_anneal_episodes = self.config['epsilon_greedy'][
'epsilon_anneal_episodes']
self.epsilon_anneal_from = self.config['epsilon_greedy'][
'epsilon_anneal_from']
self.epsilon_anneal_to = self.config['epsilon_greedy'][
'epsilon_anneal_to']
self.epsilon = self.epsilon_anneal_from
self.noisy_net = self.config['epsilon_greedy']['noisy_net']
if self.noisy_net:
# disable epsilon greedy
self.epsilon_anneal_episodes = -1
self.epsilon = 0.0
self.nlp = spacy.load('en', disable=['ner', 'parser', 'tagger'])
self.single_word_verbs = set(["inventory", "look", "wait"])
self.two_word_verbs = set(["go"])
def train(self):
"""
Tell the agent that it's training phase.
"""
self.mode = "train"
self.online_net.train()
def eval(self):
"""
Tell the agent that it's evaluation phase.
"""
self.mode = "eval"
self.online_net.eval()
def update_target_net(self):
self.target_net.load_state_dict(self.online_net.state_dict())
def reset_noise(self):
if self.noisy_net:
# Resets noisy weights in all linear layers (of online net only)
self.online_net.reset_noise()
def zero_noise(self):
if self.noisy_net:
self.online_net.zero_noise()
self.target_net.zero_noise()
def load_pretrained_model(self, load_from):
"""
Load pretrained checkpoint from file.
Arguments:
load_from: File name of the pretrained model checkpoint.
"""
print("loading model from %s\n" % (load_from))
try:
if self.use_cuda:
state_dict = torch.load(load_from)
else:
state_dict = torch.load(load_from, map_location='cpu')
self.online_net.load_state_dict(state_dict)
except:
print("Failed to load checkpoint...")
def save_model_to_path(self, save_to):
torch.save(self.online_net.state_dict(), save_to)
print("Saved checkpoint to %s..." % (save_to))
def init(self, obs, infos):
"""
Prepare the agent for the upcoming games.
Arguments:
obs: Previous command's feedback for each game.
infos: Additional information for each game.
"""
# reset agent, get vocabulary masks for verbs / adjectives / nouns
batch_size = len(obs)
self.reset_binarized_counter(batch_size)
self.not_finished_yet = np.ones((batch_size, ), dtype="float32")
self.prev_actions = [["" for _ in range(batch_size)]]
self.prev_step_is_still_interacting = np.ones(
(batch_size, ), dtype="float32"
) # 1s and starts to be 0 when previous action is "wait"
self.naozi.reset(batch_size=batch_size)
def get_agent_inputs(self, string_list):
sentence_token_list = [item.split() for item in string_list]
sentence_id_list = [
_words_to_ids(tokens, self.word2id)
for tokens in sentence_token_list
]
input_sentence_char = list_of_token_list_to_char_input(
sentence_token_list, self.char2id)
input_sentence = pad_sequences(
sentence_id_list, maxlen=max_len(sentence_id_list)).astype('int32')
input_sentence = to_pt(input_sentence, self.use_cuda)
input_sentence_char = to_pt(input_sentence_char, self.use_cuda)
return input_sentence, input_sentence_char, sentence_id_list
def get_game_info_at_certain_step(self, obs, infos):
"""
Get all needed info from game engine for training.
Arguments:
obs: Previous command's feedback for each game.
infos: Additional information for each game.
"""
batch_size = len(obs)
feedback_strings = [preproc(item, tokenizer=self.nlp) for item in obs]
description_strings = [
preproc(item, tokenizer=self.nlp) for item in infos["description"]
]
observation_strings = [
d + " <|> " + fb if fb != d else d + " <|> hello"
for fb, d in zip(feedback_strings, description_strings)
]
inventory_strings = [
preproc(item, tokenizer=self.nlp) for item in infos["inventory"]
]
local_word_list = [
obs.split() + inv.split()
for obs, inv in zip(observation_strings, inventory_strings)
]
directions = ["east", "west", "north", "south"]
if self.question_type in ["location", "existence"]:
# agents observes the env, but do not change them
possible_verbs = [["go", "inventory", "wait", "open", "examine"]
for _ in range(batch_size)]
else:
possible_verbs = [
list(set(item) - set(["", "look"])) for item in infos["verbs"]
]
possible_adjs, possible_nouns = [], []
for i in range(batch_size):
object_nouns = [
item.split()[-1] for item in infos["object_nouns"][i]
]
object_adjs = [
w for item in infos["object_adjs"][i] for w in item.split()
]
possible_nouns.append(
list(set(object_nouns) & set(local_word_list[i]) - set([""])) +
directions)
possible_adjs.append(
list(set(object_adjs) & set(local_word_list[i]) - set([""])) +
["</s>"])
return observation_strings, [
possible_verbs, possible_adjs, possible_nouns
]
def get_state_strings(self, infos):
description_strings = infos["description"]
inventory_strings = infos["inventory"]
observation_strings = [
_d + _i for (_d, _i) in zip(description_strings, inventory_strings)
]
return observation_strings
def get_local_word_masks(self, possible_words):
possible_verbs, possible_adjs, possible_nouns = possible_words
batch_size = len(possible_verbs)
verb_mask = np.zeros((batch_size, len(self.word_vocab)),
dtype="float32")
noun_mask = np.zeros((batch_size, len(self.word_vocab)),
dtype="float32")
adj_mask = np.zeros((batch_size, len(self.word_vocab)),
dtype="float32")
for i in range(batch_size):
for w in possible_verbs[i]:
if w in self.word2id:
verb_mask[i][self.word2id[w]] = 1.0
for w in possible_adjs[i]:
if w in self.word2id:
adj_mask[i][self.word2id[w]] = 1.0
for w in possible_nouns[i]:
if w in self.word2id:
noun_mask[i][self.word2id[w]] = 1.0
adj_mask[:, self.EOS_id] = 1.0
return [verb_mask, adj_mask, noun_mask]
def get_match_representations(self,
input_observation,
input_observation_char,
input_quest,
input_quest_char,
use_model="online"):
model = self.online_net if use_model == "online" else self.target_net
description_representation_sequence, description_mask = model.representation_generator(
input_observation, input_observation_char)
quest_representation_sequence, quest_mask = model.representation_generator(
input_quest, input_quest_char)
match_representation_sequence = model.get_match_representations(
description_representation_sequence, description_mask,
quest_representation_sequence, quest_mask)
match_representation_sequence = match_representation_sequence * description_mask.unsqueeze(
-1)
return match_representation_sequence
def get_ranks(self,
input_observation,
input_observation_char,
input_quest,
input_quest_char,
word_masks,
use_model="online"):
"""
Given input observation and question tensors, to get Q values of words.
"""
model = self.online_net if use_model == "online" else self.target_net
match_representation_sequence = self.get_match_representations(
input_observation,
input_observation_char,
input_quest,
input_quest_char,
use_model=use_model)
action_ranks = model.action_scorer(match_representation_sequence,
word_masks) # list of 3 tensors
return action_ranks
def choose_maxQ_command(self, action_ranks, word_mask=None):
"""
Generate a command by maximum q values, for epsilon greedy.
"""
if self.use_distributional:
action_ranks = [
(item * self.support).sum(2) for item in action_ranks
] # list of batch x n_vocab
action_indices = []
for i in range(len(action_ranks)):
ar = action_ranks[i]
ar = ar - torch.min(
ar, -1, keepdim=True
)[0] + 1e-2 # minus the min value, so that all values are non-negative
if word_mask is not None:
assert word_mask[i].size() == ar.size(), (
word_mask[i].size().shape, ar.size())
ar = ar * word_mask[i]
action_indices.append(torch.argmax(ar, -1)) # batch
return action_indices
def choose_random_command(self,
batch_size,
action_space_size,
possible_words=None):
"""
Generate a command randomly, for epsilon greedy.
"""
action_indices = []
for i in range(3):
if possible_words is None:
indices = np.random.choice(action_space_size, batch_size)
else:
indices = []
for j in range(batch_size):
mask_ids = []
for w in possible_words[i][j]:
if w in self.word2id:
mask_ids.append(self.word2id[w])
indices.append(np.random.choice(mask_ids))
indices = np.array(indices)
action_indices.append(to_pt(indices, self.use_cuda)) # batch
return action_indices
def get_chosen_strings(self, chosen_indices):
"""
Turns list of word indices into actual command strings.
chosen_indices: Word indices chosen by model.
"""
chosen_indices_np = [to_np(item) for item in chosen_indices]
res_str = []
batch_size = chosen_indices_np[0].shape[0]
for i in range(batch_size):
verb, adj, noun = chosen_indices_np[0][i], chosen_indices_np[1][
i], chosen_indices_np[2][i]
res_str.append(self.word_ids_to_commands(verb, adj, noun))
return res_str
def word_ids_to_commands(self, verb, adj, noun):
"""
Turn the 3 indices into actual command strings.
Arguments:
verb: Index of the guessing verb in vocabulary
adj: Index of the guessing adjective in vocabulary
noun: Index of the guessing noun in vocabulary
"""
# turns 3 indices into actual command strings
if self.word_vocab[verb] in self.single_word_verbs:
return self.word_vocab[verb]
if self.word_vocab[verb] in self.two_word_verbs:
return " ".join([self.word_vocab[verb], self.word_vocab[noun]])
if adj == self.EOS_id:
return " ".join([self.word_vocab[verb], self.word_vocab[noun]])
else:
return " ".join([
self.word_vocab[verb], self.word_vocab[adj],
self.word_vocab[noun]
])
def act_random(self, obs, infos, input_observation, input_observation_char,
input_quest, input_quest_char, possible_words):
with torch.no_grad():
batch_size = len(obs)
word_indices_random = self.choose_random_command(
batch_size, len(self.word_vocab), possible_words)
chosen_indices = word_indices_random
chosen_strings = self.get_chosen_strings(chosen_indices)
for i in range(batch_size):
if chosen_strings[i] == "wait":
self.not_finished_yet[i] = 0.0
# info for replay memory
for i in range(batch_size):
if self.prev_actions[-1][i] == "wait":
self.prev_step_is_still_interacting[i] = 0.0
# previous step is still interacting, this is because DQN requires one step extra computation
replay_info = [
chosen_indices,
to_pt(self.prev_step_is_still_interacting, self.use_cuda,
"float")
]
# cache new info in current game step into caches
self.prev_actions.append(chosen_strings)
return chosen_strings, replay_info
def act_greedy(self, obs, infos, input_observation, input_observation_char,
input_quest, input_quest_char, possible_words):
"""
Acts upon the current list of observations.
One text command must be returned for each observation.
"""
with torch.no_grad():
batch_size = len(obs)
local_word_masks_np = self.get_local_word_masks(possible_words)
local_word_masks = [
to_pt(item, self.use_cuda, type="float")
for item in local_word_masks_np
]
# generate commands for one game step, epsilon greedy is applied, i.e.,
# there is epsilon of chance to generate random commands
action_ranks = self.get_ranks(
input_observation,
input_observation_char,
input_quest,
input_quest_char,
local_word_masks,
use_model="online") # list of batch x vocab
word_indices_maxq = self.choose_maxQ_command(
action_ranks, local_word_masks)
chosen_indices = word_indices_maxq
chosen_strings = self.get_chosen_strings(chosen_indices)
for i in range(batch_size):
if chosen_strings[i] == "wait":
self.not_finished_yet[i] = 0.0
# info for replay memory
for i in range(batch_size):
if self.prev_actions[-1][i] == "wait":
self.prev_step_is_still_interacting[i] = 0.0
# previous step is still interacting, this is because DQN requires one step extra computation
replay_info = [
chosen_indices,
to_pt(self.prev_step_is_still_interacting, self.use_cuda,
"float")
]
# cache new info in current game step into caches
self.prev_actions.append(chosen_strings)
return chosen_strings, replay_info
def act(self,
obs,
infos,
input_observation,
input_observation_char,
input_quest,
input_quest_char,
possible_words,
random=False):
"""
Acts upon the current list of observations.
One text command must be returned for each observation.
"""
with torch.no_grad():
if self.mode == "eval":
return self.act_greedy(obs, infos, input_observation,
input_observation_char, input_quest,
input_quest_char, possible_words)
if random:
return self.act_random(obs, infos, input_observation,
input_observation_char, input_quest,
input_quest_char, possible_words)
batch_size = len(obs)
local_word_masks_np = self.get_local_word_masks(possible_words)
local_word_masks = [
to_pt(item, self.use_cuda, type="float")
for item in local_word_masks_np
]
# generate commands for one game step, epsilon greedy is applied, i.e.,
# there is epsilon of chance to generate random commands
action_ranks = self.get_ranks(
input_observation,
input_observation_char,
input_quest,
input_quest_char,
local_word_masks,
use_model="online") # list of batch x vocab
word_indices_maxq = self.choose_maxQ_command(
action_ranks, local_word_masks)
word_indices_random = self.choose_random_command(
batch_size, len(self.word_vocab), possible_words)
# random number for epsilon greedy
rand_num = np.random.uniform(low=0.0,
high=1.0,
size=(batch_size, ))
less_than_epsilon = (rand_num < self.epsilon).astype(
"float32") # batch
greater_than_epsilon = 1.0 - less_than_epsilon
less_than_epsilon = to_pt(less_than_epsilon,
self.use_cuda,
type='long')
greater_than_epsilon = to_pt(greater_than_epsilon,
self.use_cuda,
type='long')
chosen_indices = [
less_than_epsilon * idx_random +
greater_than_epsilon * idx_maxq
for idx_random, idx_maxq in zip(word_indices_random,
word_indices_maxq)
]
chosen_strings = self.get_chosen_strings(chosen_indices)
for i in range(batch_size):
if chosen_strings[i] == "wait":
self.not_finished_yet[i] = 0.0
# info for replay memory
for i in range(batch_size):
if self.prev_actions[-1][i] == "wait":
self.prev_step_is_still_interacting[i] = 0.0
# previous step is still interacting, this is because DQN requires one step extra computation
replay_info = [
chosen_indices,
to_pt(self.prev_step_is_still_interacting, self.use_cuda,
"float")
]
# cache new info in current game step into caches
self.prev_actions.append(chosen_strings)
return chosen_strings, replay_info
def get_dqn_loss(self):
"""
Update neural model in agent. In this example we follow algorithm
of updating model in dqn with replay memory.
"""
if len(self.command_generation_replay_memory) < self.replay_batch_size:
return None
data = self.command_generation_replay_memory.get_batch(
self.replay_batch_size, self.multi_step)
if data is None:
return None
obs_list, quest_list, possible_words_list, chosen_indices, rewards, next_obs_list, next_possible_words_list, actual_n_list = data
batch_size = len(actual_n_list)
input_quest, input_quest_char, _ = self.get_agent_inputs(quest_list)
input_observation, input_observation_char, _ = self.get_agent_inputs(
obs_list)
next_input_observation, next_input_observation_char, _ = self.get_agent_inputs(
next_obs_list)
possible_words, next_possible_words = [], []
for i in range(3):
possible_words.append([item[i] for item in possible_words_list])
next_possible_words.append(
[item[i] for item in next_possible_words_list])
local_word_masks = [
to_pt(item, self.use_cuda, type="float")
for item in self.get_local_word_masks(possible_words)
]
next_local_word_masks = [
to_pt(item, self.use_cuda, type="float")
for item in self.get_local_word_masks(next_possible_words)
]
action_ranks = self.get_ranks(
input_observation,
input_observation_char,
input_quest,
input_quest_char,
local_word_masks,
use_model="online"
) # list of batch x vocab or list of batch x vocab x atoms
# ps_a
word_qvalues = [
ez_gather_dim_1(w_rank, idx.unsqueeze(-1)).squeeze(1)
for w_rank, idx in zip(action_ranks, chosen_indices)
] # list of batch or list of batch x atoms
q_value = torch.mean(torch.stack(word_qvalues, -1),
-1) # batch or batch x atoms
# log_ps_a
log_q_value = torch.log(q_value) # batch or batch x atoms
with torch.no_grad():
if self.noisy_net:
self.target_net.reset_noise() # Sample new target net noise
if self.double_dqn:
# pns Probabilities p(s_t+n, ·; θonline)
next_action_ranks = self.get_ranks(next_input_observation,
next_input_observation_char,
input_quest,
input_quest_char,
next_local_word_masks,
use_model="online")
# list of batch x vocab or list of batch x vocab x atoms
# Perform argmax action selection using online network: argmax_a[(z, p(s_t+n, a; θonline))]
next_word_indices = self.choose_maxQ_command(
next_action_ranks,
next_local_word_masks) # list of batch x 1
# pns # Probabilities p(s_t+n, ·; θtarget)
next_action_ranks = self.get_ranks(
next_input_observation,
next_input_observation_char,
input_quest,
input_quest_char,
next_local_word_masks,
use_model="target"
) # batch x vocab or list of batch x vocab x atoms
# pns_a # Double-Q probabilities p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget)
next_word_qvalues = [
ez_gather_dim_1(w_rank, idx.unsqueeze(-1)).squeeze(1) for
w_rank, idx in zip(next_action_ranks, next_word_indices)
] # list of batch or list of batch x atoms
else:
# pns Probabilities p(s_t+n, ·; θonline)
next_action_ranks = self.get_ranks(next_input_observation,
next_input_observation_char,
input_quest,
input_quest_char,
next_local_word_masks,
use_model="target")
# list of batch x vocab or list of batch x vocab x atoms
next_word_indices = self.choose_maxQ_command(
next_action_ranks,
next_local_word_masks) # list of batch x 1
next_word_qvalues = [
ez_gather_dim_1(w_rank, idx.unsqueeze(-1)).squeeze(1) for
w_rank, idx in zip(next_action_ranks, next_word_indices)
] # list of batch or list of batch x atoms
next_q_value = torch.mean(torch.stack(next_word_qvalues, -1),
-1) # batch or batch x atoms
# Compute Tz (Bellman operator T applied to z)
discount = to_pt((np.ones_like(actual_n_list) *
self.discount_gamma)**actual_n_list,
self.use_cuda,
type="float")
if not self.use_distributional:
rewards = rewards + next_q_value * discount # batch
loss = F.smooth_l1_loss(q_value, rewards)
return loss
with torch.no_grad():
Tz = rewards.unsqueeze(
-1) + discount.unsqueeze(-1) * self.support.unsqueeze(
0) # Tz = R^n + (γ^n)z (accounting for terminal states)
Tz = Tz.clamp(min=self.v_min,
max=self.v_max) # Clamp between supported values
# Compute L2 projection of Tz onto fixed support z
b = (Tz - self.v_min) / 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 = torch.zeros(batch_size, self.atoms).float()
if self.use_cuda:
m = m.cuda()
offset = torch.linspace(0, ((batch_size - 1) * self.atoms),
batch_size).unsqueeze(1).expand(
batch_size, self.atoms).long()
if self.use_cuda:
offset = offset.cuda()
m.view(-1).index_add_(
0, (l + offset).view(-1),
(next_q_value *
(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),
(next_q_value *
(b - l.float())).view(-1)) # m_u = m_u + p(s_t+n, a*)(b - l)
loss = -torch.sum(
m * log_q_value,
1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t)))
loss = torch.mean(loss)
return loss
def update_interaction(self):
# update neural model by replaying snapshots in replay memory
interaction_loss = self.get_dqn_loss()
if interaction_loss is None:
return None
loss = interaction_loss * self.interaction_loss_lambda
# Backpropagate
self.online_net.zero_grad()
self.optimizer.zero_grad()
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(self.online_net.parameters(),
self.clip_grad_norm)
self.optimizer.step() # apply gradients
return to_np(torch.mean(interaction_loss))
def answer_question(self,
input_observation,
input_observation_char,
observation_id_list,
input_quest,
input_quest_char,
use_model="online"):
# first pad answerer_input, and get the mask
model = self.online_net if use_model == "online" else self.target_net
batch_size = len(observation_id_list)
max_length = input_observation.size(1)
mask = compute_mask(input_observation) # batch x obs_len
# noun mask for location question
if self.question_type in ["location"]:
location_mask = []
for i in range(batch_size):
m = [1 for item in observation_id_list[i]]
location_mask.append(m)
location_mask = pad_sequences(location_mask,
maxlen=max_length,
dtype="float32")
location_mask = to_pt(location_mask,
enable_cuda=self.use_cuda,
type='float')
assert mask.size() == location_mask.size()
mask = mask * location_mask
match_representation_sequence = self.get_match_representations(
input_observation,
input_observation_char,
input_quest,
input_quest_char,
use_model=use_model)
pred = model.answer_question(match_representation_sequence,
mask) # batch x vocab or batch x 2
# attention sum:
# sometimes certain word appears multiple times in the observation,
# thus we need to merge them together before doing further computations
# ------- but
# if answer type is not pointing, we just use a pre-defined mapping
# that maps 0/1 to their positions in vocab
if self.answer_type == "2 way":
observation_id_list = []
max_length = 2
for i in range(batch_size):
observation_id_list.append(
[self.word2id["0"], self.word2id["1"]])
observation = to_pt(
pad_sequences(observation_id_list,
maxlen=max_length).astype('int32'), self.use_cuda)
vocab_distribution = np.zeros(
(batch_size, len(self.word_vocab))) # batch x vocab
vocab_distribution = to_pt(vocab_distribution,
self.use_cuda,
type='float')
vocab_distribution = vocab_distribution.scatter_add_(
1, observation, pred) # batch x vocab
non_zero_words = []
for i in range(batch_size):
non_zero_words.append(list(set(observation_id_list[i])))
vocab_mask = torch.ne(vocab_distribution, 0).float()
return vocab_distribution, non_zero_words, vocab_mask
def point_maxq_position(self, vocab_distribution, mask):
"""
Generate a command by maximum q values, for epsilon greedy.
Arguments:
point_distribution: Q values for each position (mapped to vocab).
mask: vocab masks.
"""
vocab_distribution = vocab_distribution - torch.min(
vocab_distribution, -1, keepdim=True
)[0] + 1e-2 # minus the min value, so that all values are non-negative
vocab_distribution = vocab_distribution * mask # batch x vocab
indices = torch.argmax(vocab_distribution, -1) # batch
return indices
def answer_question_act_greedy(self, input_observation,
input_observation_char, observation_id_list,
input_quest, input_quest_char):
with torch.no_grad():
vocab_distribution, _, vocab_mask = self.answer_question(
input_observation,
input_observation_char,
observation_id_list,
input_quest,
input_quest_char,
use_model="online") # batch x time
positions_maxq = self.point_maxq_position(vocab_distribution,
vocab_mask)
return positions_maxq # batch
def get_qa_loss(self):
"""
Update neural model in agent. In this example we follow algorithm
of updating model in dqn with replay memory.
"""
if len(self.qa_replay_memory) < self.replay_batch_size:
return None
transitions = self.qa_replay_memory.sample(self.replay_batch_size)
batch = qa_memory.qa_Transition(*zip(*transitions))
observation_list = batch.observation_list
quest_list = batch.quest_list
answer_strings = batch.answer_strings
answer_position = np.array(_words_to_ids(answer_strings, self.word2id))
groundtruth = to_pt(answer_position, self.use_cuda) # batch
input_quest, input_quest_char, _ = self.get_agent_inputs(quest_list)
input_observation, input_observation_char, observation_id_list = self.get_agent_inputs(
observation_list)
answer_distribution, _, _ = self.answer_question(
input_observation,
input_observation_char,
observation_id_list,
input_quest,
input_quest_char,
use_model="online") # batch x vocab
batch_loss = NegativeLogLoss(answer_distribution, groundtruth) # batch
return torch.mean(batch_loss)
def update_qa(self):
# update neural model by replaying snapshots in replay memory
qa_loss = self.get_qa_loss()
if qa_loss is None:
return None
loss = qa_loss * self.qa_loss_lambda
# Backpropagate
self.online_net.zero_grad()
self.optimizer.zero_grad()
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(self.online_net.parameters(),
self.clip_grad_norm)
self.optimizer.step() # apply gradients
return to_np(torch.mean(qa_loss))
def finish_of_episode(self, episode_no, batch_size):
# Update target networt
if (
episode_no + batch_size
) % self.target_net_update_frequency <= episode_no % self.target_net_update_frequency:
self.update_target_net()
# decay lambdas
if episode_no < self.learn_start_from_this_episode:
return
if episode_no < self.epsilon_anneal_episodes + self.learn_start_from_this_episode:
self.epsilon -= (self.epsilon_anneal_from - self.epsilon_anneal_to
) / float(self.epsilon_anneal_episodes)
self.epsilon = max(self.epsilon, 0.0)
if episode_no < self.revisit_counting_lambda_anneal_episodes + self.learn_start_from_this_episode:
self.revisit_counting_lambda -= (
self.revisit_counting_lambda_anneal_from -
self.revisit_counting_lambda_anneal_to) / float(
self.revisit_counting_lambda_anneal_episodes)
self.revisit_counting_lambda = max(self.epsilon, 0.0)
def reset_binarized_counter(self, batch_size):
self.binarized_counter_dict = [{} for _ in range(batch_size)]
def get_binarized_count(self, observation_strings, update=True):
count_rewards = []
batch_size = len(observation_strings)
for i in range(batch_size):
key = observation_strings[i]
if key not in self.binarized_counter_dict[i]:
self.binarized_counter_dict[i][key] = 0.0
if update:
self.binarized_counter_dict[i][key] += 1.0
r = self.binarized_counter_dict[i][key]
r = float(r == 1.0)
count_rewards.append(r)
return count_rewards
class Agent():
def __init__(self, args, env):
self.action_space = env.action_space()
self.atoms = args.atoms # size of value distribution.
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)
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) # greedily selects the action.
if args.model and os.path.isfile(args.model):
self.online_net.load_state_dict(
torch.load(args.model, map_location='cpu')
) # state_dict: python dictionary that maps each layer to its parameters.
self.online_net.train()
self.target_net = DQN(args, self.action_space).to(
device=args.device) # use to compute target q-values.
self.update_target_net(
) # sets it to the parameters of the online network.
self.target_net.train()
for param in self.target_net.parameters(
): # not updated through backpropagation.
param.requires_grad = False
self.optimiser = optim.Adam(self.online_net.parameters(),
lr=args.lr,
eps=args.adam_eps)
def reset_noise(self):
self.online_net.reset_noise()
def act(self, state):
with torch.no_grad():
return (self.online_net(state.unsqueeze(0)) *
self.support).sum(2).argmax(1).item()
def act_e_greedy(self, state, epsilon=0.001):
return np.random.randint(
0, self.action_space
) if np.random.random() < epsilon else self.act(state)
def learn(self, mem):
idxs, states, actions, returns, next_states, nonterminals, weights = mem.sample(
self.batch_size)
log_ps = self.online_net(states, log=True)
log_ps_a = log_ps[range(self.batch_size), actions]
with torch.no_grad():
pns = self.online_net(next_states)
dns = self.support.expand_as(pns) * pns
argmax_indices_ns = dns.sum(2).argmax(1)
self.target_net.reset_noise()
pns = self.target_net(next_states)
pns_a = pns[range(self.batch_size), argmax_indices_ns]
Tz = returns.unsqueeze(1) + nonterminals * (
self.discount**self.n) * self.support.unsqueeze(0)
Tz = Tz.clamp(min=self.Vmin, max=self.Vmax)
b = (Tz - self.Vmin) / self.delta_z
l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64)
l[(u > 0) * (l == u)] -= 1
u[(l < (self.atoms - 1)) * (l == u)] += 1
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.view(-1).index_add_(0, (u + offset).view(-1),
(pns_a * (b - l.float())).view(-1))
loss = -torch.sum(m * log_ps_a, 1)
self.online_net.zero_grad()
(weights * loss).mean().backward()
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 save(self, path):
torch.save(self.online_net.state_dict(),
os.path.join(path, 'model_all_layers.pth'))
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 Agent():
def __init__(self, args, env):
self.action_space = env.action_space()
self.quantile = args.quantile
self.atoms = args.quantiles if args.quantile else args.atoms
if args.quantile:
self.cumulative_density = (2 * torch.arange(self.atoms).to(device=args.device) + 1) / (2 * self.atoms) # Quantile cumulative probability weights τ
else:
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, args.quantile).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, args.quantile).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)) * ((1 / self.atoms) if self.quantile else self.support)).sum(2).argmax(1).item()
# Acts with an ε-greedy policy
def act_e_greedy(self, state, epsilon=0.05):
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)
ps = self.online_net(states) # Probabilities p(s_t, ·; θonline)/quantile probabilities θ(s_t, ·; θonline)
ps_a = ps[range(self.batch_size), actions] # 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 = ((1 / self.atoms) if self.quantile else 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)
if self.quantile:
# Compute distributional Bellman target Tθ = R^n + (γ^n)p(s_t+n, argmax_a[(z, p(s_t+n, a; θonline))]; θtarget)
Ttheta = returns.unsqueeze(1) + nonterminals * (self.discount ** self.n) * pns_a # (accounting for terminal states)
else:
# 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)
if self.quantile:
u = Ttheta - ps_a # Residual u
kappa_cond = (u < 1).to(torch.float32) # |u| ≤ κ
huber_loss = 0.5 * u ** 2 * kappa_cond + (u.abs() - 0.5) * (1 - kappa_cond) # Huber loss Lκ(u)
loss = torch.sum(torch.abs(self.cumulative_density - (u < 0).to(torch.float32)) * huber_loss, 1) # Quantile Huber loss ρκτ(u) = |τ − δ{u<0}|Lκ(u)
else:
loss = -torch.sum(m * ps_a.log(), 1) # Cross-entropy loss (minimises DKL(m||p(s_t, a_t)))
loss = weights * loss # Importance weight losses
self.online_net.zero_grad()
loss.mean().backward() # Backpropagate minibatch loss
self.optimiser.step()
nn.utils.clip_grad_norm_(self.online_net.parameters(), self.norm_clip) # Clip gradients by L2 norm
if self.quantile:
loss = (self.atoms * loss).clamp(max=5) # Heuristic for prioritised replay
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)) * ((1 / self.atoms) if self.quantile else 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.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.saved_model_path = args.saved_model_path
self.experiment = args.experiment
self.plots_path = args.plots_path
self.data_save_path = args.data_save_path
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)
# list of layers:
self.online_net_layers = [self.online_net.conv1,
self.online_net.conv2,
self.online_net.conv3,
self.online_net.fc_h_v,
self.online_net.fc_h_a,
self.online_net.fc_z_v,
self.online_net.fc_z_a
]
self.target_net_layers = [self.target_net.conv1,
self.target_net.conv2,
self.target_net.conv3,
self.target_net.fc_h_v,
self.target_net.fc_h_a,
self.target_net.fc_z_v,
self.target_net.fc_z_a
]
# freeze all layers except the last, and reinitialize last
if args.freeze_layers > 0:
self.freeze_layers(args.freeze_layers)
if args.reinitialize_layers > 0:
self.reinit_layers(args.reinitialize_layers)
# 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 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)
# 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().cpu().numpy()) # 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, self.experiment + '_model.pth')) # '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()
def freeze_layers(self, num_frozen_layers):
# reinitialize the proper layers (all that were not frozen
self.reinit_layers(5 - num_frozen_layers)
for i in range(num_frozen_layers):
if i == 0:
# freeze last layer (two in list)
self.online_net_layers[0].weight.requires_grad = False
self.online_net_layers[0].bias.requires_grad = False
elif i == 1:
self.online_net_layers[1].weight.requires_grad = False
self.online_net_layers[1].bias.requires_grad = False
elif i == 2:
self.online_net_layers[2].weight.requires_grad = False
self.online_net_layers[2].bias.requires_grad = False
elif i == 3:
self.online_net_layers[3].weight_mu.requires_grad = False
self.online_net_layers[3].weight_sigma.requires_grad = False
self.online_net_layers[3].bias_mu.requires_grad = False
self.online_net_layers[3].bias_sigma.requires_grad = False
# self.online_net_layers[3].weight.requires_grad = False
self.online_net_layers[4].bias_mu.requires_grad = False
self.online_net_layers[4].bias_sigma.requires_grad = False
self.online_net_layers[4].weight_mu.requires_grad = False
self.online_net_layers[4].weight_sigma.requires_grad = False
# self.online_net_layers[4].bias.requires_grad = False
# elif i == 4:
# self.online_net_layers[0].reset_parameters()
# self.target_net_layers[0].reset_parameters()
# freeze the proper layers - complicated work around for dueling architecture
# ct = 0
# fourth_layer_first_time = True
# for child in self.online_net.children():
# if ct < num_frozen_layers and ct < 3:
# for param in child.parameters():
# print('something1')
# param.required_grad = False
# if ct < num_frozen_layers and ct == 3:
# for param in child.parameters():
# print('something2')
# param.required_grad = False
# if fourth_layer_first_time:
# fourth_layer_first_time = False
# ct -= 1
# ct += 1
#
# ct = 0
# fourth_layer_first_time = True
# for child in self.target_net.children():
# if ct < num_frozen_layers and ct < 3:
# for param in child.parameters():
# print('something3')
# param.required_grad = False
# if ct < num_frozen_layers and ct == 3:
# for param in child.parameters():
# print('something4')
# param.required_grad = False
# if fourth_layer_first_time:
# fourth_layer_first_time = False
# ct -= 1
# ct += 1
print(self.online_net)
print(list(i.requires_grad for i in self.online_net.parameters()))
print(self.target_net)
print(list(i.requires_grad for i in self.target_net.parameters()))
def reinit_layers(self, num_layers):
for i in range(num_layers):
if i == 0:
# freeze last layer (two in list)
self.online_net_layers[6].reset_parameters()
self.online_net_layers[5].reset_parameters()
self.target_net_layers[6].reset_parameters()
self.target_net_layers[5].reset_parameters()
elif i == 1:
self.online_net_layers[4].reset_parameters()
self.online_net_layers[3].reset_parameters()
self.target_net_layers[4].reset_parameters()
self.target_net_layers[3].reset_parameters()
elif i == 2:
self.online_net_layers[2].reset_parameters()
self.target_net_layers[2].reset_parameters()
elif i == 3:
self.online_net_layers[1].reset_parameters()
self.target_net_layers[1].reset_parameters()
elif i == 4:
self.online_net_layers[0].reset_parameters()
self.target_net_layers[0].reset_parameters()
