def __init__(self,
q_t_selected,
q_tp1_best,
importance_weights,
rewards,
done_mask,
gamma=0.99,
n_step=1,
num_atoms=1,
v_min=-10.0,
v_max=10.0):
if num_atoms > 1:
raise ValueError("Torch version of DQN does not support "
"distributional Q yet!")
q_tp1_best_masked = (1.0 - done_mask) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = rewards + gamma**n_step * q_tp1_best_masked
# compute the error (potentially clipped)
self.td_error = q_t_selected - q_t_selected_target.detach()
self.loss = torch.mean(importance_weights.float() *
huber_loss(self.td_error))
self.stats = {
"mean_q": torch.mean(q_t_selected),
"min_q": torch.min(q_t_selected),
"max_q": torch.max(q_t_selected),
"td_error": self.td_error,
"mean_td_error": torch.mean(self.td_error),
}
def build_q_losses(policy: Policy, model, dist_class,
train_batch: SampleBatch) -> TensorType:
"""Constructs the loss for SimpleQTorchPolicy.
Args:
policy (Policy): The Policy to calculate the loss for.
model (ModelV2): The Model to calculate the loss for.
dist_class (Type[ActionDistribution]): The action distribution class.
train_batch (SampleBatch): The training data.
Returns:
TensorType: A single loss tensor.
"""
target_model = policy.target_models[model]
# q network evaluation
q_t = compute_q_values(policy,
model,
train_batch[SampleBatch.CUR_OBS],
explore=False,
is_training=True)
# target q network evalution
q_tp1 = compute_q_values(policy,
target_model,
train_batch[SampleBatch.NEXT_OBS],
explore=False,
is_training=True)
# q scores for actions which we know were selected in the given state.
one_hot_selection = F.one_hot(train_batch[SampleBatch.ACTIONS].long(),
policy.action_space.n)
q_t_selected = torch.sum(q_t * one_hot_selection, 1)
# compute estimate of best possible value starting from state at t + 1
dones = train_batch[SampleBatch.DONES].float()
q_tp1_best_one_hot_selection = F.one_hot(torch.argmax(q_tp1, 1),
policy.action_space.n)
q_tp1_best = torch.sum(q_tp1 * q_tp1_best_one_hot_selection, 1)
q_tp1_best_masked = (1.0 - dones) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = (train_batch[SampleBatch.REWARDS] +
policy.config["gamma"] * q_tp1_best_masked)
# Compute the error (Square/Huber).
td_error = q_t_selected - q_t_selected_target.detach()
loss = torch.mean(huber_loss(td_error))
# Store values for stats function in model (tower), such that for
# multi-GPU, we do not override them during the parallel loss phase.
model.tower_stats["loss"] = loss
# TD-error tensor in final stats
# will be concatenated and retrieved for each individual batch item.
model.tower_stats["td_error"] = td_error
return loss
def build_q_losses(policy: Policy, model, dist_class,
train_batch: SampleBatch) -> TensorType:
"""Constructs the loss for SimpleQTorchPolicy.
Args:
policy (Policy): The Policy to calculate the loss for.
model (ModelV2): The Model to calculate the loss for.
dist_class (Type[ActionDistribution]): The action distribution class.
train_batch (SampleBatch): The training data.
Returns:
TensorType: A single loss tensor.
"""
target_model = policy.target_models[model]
# q network evaluation
q_t = compute_q_values(policy,
model,
train_batch[SampleBatch.CUR_OBS],
explore=False,
is_training=True)
# target q network evalution
q_tp1 = compute_q_values(policy,
target_model,
train_batch[SampleBatch.NEXT_OBS],
explore=False,
is_training=True)
# q scores for actions which we know were selected in the given state.
one_hot_selection = F.one_hot(train_batch[SampleBatch.ACTIONS].long(),
policy.action_space.n)
q_t_selected = torch.sum(q_t * one_hot_selection, 1)
# compute estimate of best possible value starting from state at t + 1
dones = train_batch[SampleBatch.DONES].float()
q_tp1_best_one_hot_selection = F.one_hot(torch.argmax(q_tp1, 1),
policy.action_space.n)
q_tp1_best = torch.sum(q_tp1 * q_tp1_best_one_hot_selection, 1)
q_tp1_best_masked = (1.0 - dones) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = (train_batch[SampleBatch.REWARDS] +
policy.config["gamma"] * q_tp1_best_masked)
# Compute the error (Square/Huber).
td_error = q_t_selected - q_t_selected_target.detach()
loss = torch.mean(huber_loss(td_error))
# save TD error as an attribute for outside access
policy.td_error = td_error
return loss
def q_loss(q_prev, q_next, next_action_q, train_batch, gamma, n_steps):
lhs = Loss.choose(q_prev,
train_batch[SampleBatch.ACTIONS].reshape(-1, 1))
a_next = t.argmax(next_action_q, 1, keepdim=True)
rhs = (train_batch[SampleBatch.REWARDS] +
gamma**n_steps * Loss.choose(q_next, a_next) *
(1.0 - train_batch[SampleBatch.DONES].float()))
td_error = lhs - rhs.detach()
huber = huber_loss(td_error)
loss = huber # * train_batch['weights']
return loss.mean(), td_error
def build_q_losses(policy, model, dist_class, train_batch):
# q network evaluation
q_t = compute_q_values(
policy,
policy.q_model,
train_batch[SampleBatch.CUR_OBS],
explore=False,
is_training=True)
# target q network evalution
q_tp1 = compute_q_values(
policy,
policy.target_q_model,
train_batch[SampleBatch.NEXT_OBS],
explore=False,
is_training=True)
# q scores for actions which we know were selected in the given state.
one_hot_selection = F.one_hot(train_batch[SampleBatch.ACTIONS],
policy.action_space.n)
q_t_selected = torch.sum(q_t * one_hot_selection, 1)
# compute estimate of best possible value starting from state at t + 1
dones = train_batch[SampleBatch.DONES].float()
q_tp1_best_one_hot_selection = F.one_hot(
torch.argmax(q_tp1, 1), policy.action_space.n)
q_tp1_best = torch.sum(q_tp1 * q_tp1_best_one_hot_selection, 1)
q_tp1_best_masked = (1.0 - dones) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = (train_batch[SampleBatch.REWARDS] +
policy.config["gamma"] * q_tp1_best_masked)
# Compute the error (Square/Huber).
td_error = q_t_selected - q_t_selected_target.detach()
loss = torch.mean(huber_loss(td_error))
# save TD error as an attribute for outside access
policy.td_error = td_error
return loss
def r2d2_loss(policy: Policy, model, _,
train_batch: SampleBatch) -> TensorType:
"""Constructs the loss for R2D2TorchPolicy.
Args:
policy (Policy): The Policy to calculate the loss for.
model (ModelV2): The Model to calculate the loss for.
train_batch (SampleBatch): The training data.
Returns:
TensorType: A single loss tensor.
"""
config = policy.config
# Construct internal state inputs.
i = 0
state_batches = []
while "state_in_{}".format(i) in train_batch:
state_batches.append(train_batch["state_in_{}".format(i)])
i += 1
assert state_batches
# Q-network evaluation (at t).
q, _, _, _ = compute_q_values(policy,
model,
train_batch,
state_batches=state_batches,
seq_lens=train_batch.get("seq_lens"),
explore=False,
is_training=True)
# Target Q-network evaluation (at t+1).
q_target, _, _, _ = compute_q_values(policy,
policy.target_q_model,
train_batch,
state_batches=state_batches,
seq_lens=train_batch.get("seq_lens"),
explore=False,
is_training=True)
actions = train_batch[SampleBatch.ACTIONS].long()
dones = train_batch[SampleBatch.DONES].float()
rewards = train_batch[SampleBatch.REWARDS]
weights = train_batch[PRIO_WEIGHTS]
B = state_batches[0].shape[0]
T = q.shape[0] // B
# Q scores for actions which we know were selected in the given state.
one_hot_selection = F.one_hot(actions, policy.action_space.n)
q_selected = torch.sum(
torch.where(q > FLOAT_MIN, q, torch.tensor(0.0, device=policy.device))
* one_hot_selection, 1)
if config["double_q"]:
best_actions = torch.argmax(q, dim=1)
else:
best_actions = torch.argmax(q_target, dim=1)
best_actions_one_hot = F.one_hot(best_actions, policy.action_space.n)
q_target_best = torch.sum(
torch.where(q_target > FLOAT_MIN, q_target,
torch.tensor(0.0, device=policy.device)) *
best_actions_one_hot,
dim=1)
if config["num_atoms"] > 1:
raise ValueError("Distributional R2D2 not supported yet!")
else:
q_target_best_masked_tp1 = (1.0 - dones) * torch.cat(
[q_target_best[1:],
torch.tensor([0.0], device=policy.device)])
if config["use_h_function"]:
h_inv = h_inverse(q_target_best_masked_tp1,
config["h_function_epsilon"])
target = h_function(
rewards + config["gamma"]**config["n_step"] * h_inv,
config["h_function_epsilon"])
else:
target = rewards + \
config["gamma"] ** config["n_step"] * q_target_best_masked_tp1
# Seq-mask all loss-related terms.
seq_mask = sequence_mask(train_batch["seq_lens"], T)[:, :-1]
# Mask away also the burn-in sequence at the beginning.
burn_in = policy.config["burn_in"]
if burn_in > 0 and burn_in < T:
seq_mask[:, :burn_in] = False
num_valid = torch.sum(seq_mask)
def reduce_mean_valid(t):
return torch.sum(t[seq_mask]) / num_valid
# Make sure use the correct time indices:
# Q(t) - [gamma * r + Q^(t+1)]
q_selected = q_selected.reshape([B, T])[:, :-1]
td_error = q_selected - target.reshape([B, T])[:, :-1].detach()
td_error = td_error * seq_mask
weights = weights.reshape([B, T])[:, :-1]
policy._total_loss = reduce_mean_valid(weights * huber_loss(td_error))
policy._td_error = td_error.reshape([-1])
policy._loss_stats = {
"mean_q": reduce_mean_valid(q_selected),
"min_q": torch.min(q_selected),
"max_q": torch.max(q_selected),
"mean_td_error": reduce_mean_valid(td_error),
}
return policy._total_loss
def __init__(self,
q_t_selected: TensorType,
q_logits_t_selected: TensorType,
q_tp1_best: TensorType,
q_probs_tp1_best: TensorType,
importance_weights: TensorType,
rewards: TensorType,
done_mask: TensorType,
gamma=0.99,
n_step=1,
num_atoms=1,
v_min=-10.0,
v_max=10.0):
if num_atoms > 1:
# Distributional Q-learning which corresponds to an entropy loss
z = torch.range(0.0, num_atoms - 1,
dtype=torch.float32).to(rewards.device)
z = v_min + z * (v_max - v_min) / float(num_atoms - 1)
# (batch_size, 1) * (1, num_atoms) = (batch_size, num_atoms)
r_tau = torch.unsqueeze(
rewards, -1) + gamma**n_step * torch.unsqueeze(
1.0 - done_mask, -1) * torch.unsqueeze(z, 0)
r_tau = torch.clamp(r_tau, v_min, v_max)
b = (r_tau - v_min) / ((v_max - v_min) / float(num_atoms - 1))
lb = torch.floor(b)
ub = torch.ceil(b)
# Indispensable judgement which is missed in most implementations
# when b happens to be an integer, lb == ub, so pr_j(s', a*) will
# be discarded because (ub-b) == (b-lb) == 0.
floor_equal_ceil = (ub - lb < 0.5).float()
# (batch_size, num_atoms, num_atoms)
l_project = F.one_hot(lb.long(), num_atoms)
# (batch_size, num_atoms, num_atoms)
u_project = F.one_hot(ub.long(), num_atoms)
ml_delta = q_probs_tp1_best * (ub - b + floor_equal_ceil)
mu_delta = q_probs_tp1_best * (b - lb)
ml_delta = torch.sum(l_project * torch.unsqueeze(ml_delta, -1),
dim=1)
mu_delta = torch.sum(u_project * torch.unsqueeze(mu_delta, -1),
dim=1)
m = ml_delta + mu_delta
# Rainbow paper claims that using this cross entropy loss for
# priority is robust and insensitive to `prioritized_replay_alpha`
self.td_error = softmax_cross_entropy_with_logits(
logits=q_logits_t_selected, labels=m)
self.loss = torch.mean(self.td_error * importance_weights)
self.stats = {
# TODO: better Q stats for dist dqn
"mean_td_error": torch.mean(self.td_error),
}
else:
q_tp1_best_masked = (1.0 - done_mask) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = rewards + gamma**n_step * q_tp1_best_masked
# compute the error (potentially clipped)
self.td_error = q_t_selected - q_t_selected_target.detach()
self.loss = torch.mean(importance_weights.float() *
huber_loss(self.td_error))
self.stats = {
"mean_q": torch.mean(q_t_selected),
"min_q": torch.min(q_t_selected),
"max_q": torch.max(q_t_selected),
"mean_td_error": torch.mean(self.td_error),
}
def actor_critic_loss(
policy: Policy, model: ModelV2,
dist_class: Type[TorchDistributionWrapper],
train_batch: SampleBatch) -> Union[TensorType, List[TensorType]]:
"""Constructs the loss for the Soft Actor Critic.
Args:
policy (Policy): The Policy to calculate the loss for.
model (ModelV2): The Model to calculate the loss for.
dist_class (Type[TorchDistributionWrapper]: The action distr. class.
train_batch (SampleBatch): The training data.
Returns:
Union[TensorType, List[TensorType]]: A single loss tensor or a list
of loss tensors.
"""
# Should be True only for debugging purposes (e.g. test cases)!
deterministic = policy.config["_deterministic_loss"]
i = 0
state_batches = []
while "state_in_{}".format(i) in train_batch:
state_batches.append(train_batch["state_in_{}".format(i)])
i += 1
assert state_batches
seq_lens = train_batch.get("seq_lens")
model_out_t, state_in_t = model(
{
"obs": train_batch[SampleBatch.CUR_OBS],
"prev_actions": train_batch[SampleBatch.PREV_ACTIONS],
"prev_rewards": train_batch[SampleBatch.PREV_REWARDS],
"is_training": True,
}, state_batches, seq_lens)
states_in_t = model.select_state(state_in_t, ["policy", "q", "twin_q"])
model_out_tp1, state_in_tp1 = model(
{
"obs": train_batch[SampleBatch.NEXT_OBS],
"prev_actions": train_batch[SampleBatch.ACTIONS],
"prev_rewards": train_batch[SampleBatch.REWARDS],
"is_training": True,
}, state_batches, seq_lens)
states_in_tp1 = model.select_state(state_in_tp1, ["policy", "q", "twin_q"])
target_model_out_tp1, target_state_in_tp1 = policy.target_model(
{
"obs": train_batch[SampleBatch.NEXT_OBS],
"prev_actions": train_batch[SampleBatch.ACTIONS],
"prev_rewards": train_batch[SampleBatch.REWARDS],
"is_training": True,
}, state_batches, seq_lens)
target_states_in_tp1 = \
policy.target_model.select_state(state_in_tp1,
["policy", "q", "twin_q"])
alpha = torch.exp(model.log_alpha)
# Discrete case.
if model.discrete:
# Get all action probs directly from pi and form their logp.
log_pis_t = F.log_softmax(model.get_policy_output(
model_out_t, states_in_t["policy"], seq_lens)[0],
dim=-1)
policy_t = torch.exp(log_pis_t)
log_pis_tp1 = F.log_softmax(
model.get_policy_output(model_out_tp1, states_in_tp1["policy"],
seq_lens)[0], -1)
policy_tp1 = torch.exp(log_pis_tp1)
# Q-values.
q_t = model.get_q_values(model_out_t, states_in_t["q"], seq_lens)[0]
# Target Q-values.
q_tp1 = policy.target_model.get_q_values(target_model_out_tp1,
target_states_in_tp1["q"],
seq_lens)[0]
if policy.config["twin_q"]:
twin_q_t = model.get_twin_q_values(model_out_t,
states_in_t["twin_q"],
seq_lens)[0]
twin_q_tp1 = policy.target_model.get_twin_q_values(
target_model_out_tp1, target_states_in_tp1["twin_q"],
seq_lens)[0]
q_tp1 = torch.min(q_tp1, twin_q_tp1)
q_tp1 -= alpha * log_pis_tp1
# Actually selected Q-values (from the actions batch).
one_hot = F.one_hot(train_batch[SampleBatch.ACTIONS].long(),
num_classes=q_t.size()[-1])
q_t_selected = torch.sum(q_t * one_hot, dim=-1)
if policy.config["twin_q"]:
twin_q_t_selected = torch.sum(twin_q_t * one_hot, dim=-1)
# Discrete case: "Best" means weighted by the policy (prob) outputs.
q_tp1_best = torch.sum(torch.mul(policy_tp1, q_tp1), dim=-1)
q_tp1_best_masked = \
(1.0 - train_batch[SampleBatch.DONES].float()) * \
q_tp1_best
# Continuous actions case.
else:
# Sample single actions from distribution.
action_dist_class = _get_dist_class(policy, policy.config,
policy.action_space)
action_dist_t = action_dist_class(
model.get_policy_output(model_out_t, states_in_t["policy"],
seq_lens)[0], policy.model)
policy_t = action_dist_t.sample() if not deterministic else \
action_dist_t.deterministic_sample()
log_pis_t = torch.unsqueeze(action_dist_t.logp(policy_t), -1)
action_dist_tp1 = action_dist_class(
model.get_policy_output(model_out_tp1, states_in_tp1["policy"],
seq_lens)[0], policy.model)
policy_tp1 = action_dist_tp1.sample() if not deterministic else \
action_dist_tp1.deterministic_sample()
log_pis_tp1 = torch.unsqueeze(action_dist_tp1.logp(policy_tp1), -1)
# Q-values for the actually selected actions.
q_t = model.get_q_values(model_out_t, states_in_t["q"], seq_lens,
train_batch[SampleBatch.ACTIONS])[0]
if policy.config["twin_q"]:
twin_q_t = model.get_twin_q_values(
model_out_t, states_in_t["twin_q"], seq_lens,
train_batch[SampleBatch.ACTIONS])[0]
# Q-values for current policy in given current state.
q_t_det_policy = model.get_q_values(model_out_t, states_in_t["q"],
seq_lens, policy_t)[0]
if policy.config["twin_q"]:
twin_q_t_det_policy = model.get_twin_q_values(
model_out_t, states_in_t["twin_q"], seq_lens, policy_t)[0]
q_t_det_policy = torch.min(q_t_det_policy, twin_q_t_det_policy)
# Target q network evaluation.
q_tp1 = policy.target_model.get_q_values(target_model_out_tp1,
target_states_in_tp1["q"],
seq_lens, policy_tp1)[0]
if policy.config["twin_q"]:
twin_q_tp1 = policy.target_model.get_twin_q_values(
target_model_out_tp1, target_states_in_tp1["twin_q"], seq_lens,
policy_tp1)[0]
# Take min over both twin-NNs.
q_tp1 = torch.min(q_tp1, twin_q_tp1)
q_t_selected = torch.squeeze(q_t, dim=-1)
if policy.config["twin_q"]:
twin_q_t_selected = torch.squeeze(twin_q_t, dim=-1)
q_tp1 -= alpha * log_pis_tp1
q_tp1_best = torch.squeeze(input=q_tp1, dim=-1)
q_tp1_best_masked = \
(1.0 - train_batch[SampleBatch.DONES].float()) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = (train_batch[SampleBatch.REWARDS] +
(policy.config["gamma"]**policy.config["n_step"]) *
q_tp1_best_masked).detach()
# BURNIN #
B = state_batches[0].shape[0]
T = q_t_selected.shape[0] // B
seq_mask = sequence_mask(train_batch["seq_lens"], T)
# Mask away also the burn-in sequence at the beginning.
burn_in = policy.config["burn_in"]
if burn_in > 0 and burn_in < T:
seq_mask[:, :burn_in] = False
seq_mask = seq_mask.reshape(-1)
num_valid = torch.sum(seq_mask)
def reduce_mean_valid(t):
return torch.sum(t[seq_mask]) / num_valid
# Compute the TD-error (potentially clipped).
base_td_error = torch.abs(q_t_selected - q_t_selected_target)
if policy.config["twin_q"]:
twin_td_error = torch.abs(twin_q_t_selected - q_t_selected_target)
td_error = 0.5 * (base_td_error + twin_td_error)
else:
td_error = base_td_error
critic_loss = [
reduce_mean_valid(train_batch[PRIO_WEIGHTS] *
huber_loss(base_td_error))
]
if policy.config["twin_q"]:
critic_loss.append(
reduce_mean_valid(train_batch[PRIO_WEIGHTS] *
huber_loss(twin_td_error)))
# Alpha- and actor losses.
# Note: In the papers, alpha is used directly, here we take the log.
# Discrete case: Multiply the action probs as weights with the original
# loss terms (no expectations needed).
if model.discrete:
weighted_log_alpha_loss = policy_t.detach() * (
-model.log_alpha * (log_pis_t + model.target_entropy).detach())
# Sum up weighted terms and mean over all batch items.
alpha_loss = reduce_mean_valid(
torch.sum(weighted_log_alpha_loss, dim=-1))
# Actor loss.
actor_loss = reduce_mean_valid(
torch.sum(
torch.mul(
# NOTE: No stop_grad around policy output here
# (compare with q_t_det_policy for continuous case).
policy_t,
alpha.detach() * log_pis_t - q_t.detach()),
dim=-1))
else:
alpha_loss = -reduce_mean_valid(
model.log_alpha * (log_pis_t + model.target_entropy).detach())
# Note: Do not detach q_t_det_policy here b/c is depends partly
# on the policy vars (policy sample pushed through Q-net).
# However, we must make sure `actor_loss` is not used to update
# the Q-net(s)' variables.
actor_loss = reduce_mean_valid(alpha.detach() * log_pis_t -
q_t_det_policy)
# Save for stats function.
policy.q_t = q_t * seq_mask[..., None]
policy.policy_t = policy_t * seq_mask[..., None]
policy.log_pis_t = log_pis_t * seq_mask[..., None]
# Store td-error in model, such that for multi-GPU, we do not override
# them during the parallel loss phase. TD-error tensor in final stats
# can then be concatenated and retrieved for each individual batch item.
model.td_error = td_error * seq_mask
policy.actor_loss = actor_loss
policy.critic_loss = critic_loss
policy.alpha_loss = alpha_loss
policy.log_alpha_value = model.log_alpha
policy.alpha_value = alpha
policy.target_entropy = model.target_entropy
# Return all loss terms corresponding to our optimizers.
return tuple([policy.actor_loss] + policy.critic_loss +
[policy.alpha_loss])
def ddpg_actor_critic_loss(policy: Policy, model: ModelV2, _,
train_batch: SampleBatch) -> TensorType:
target_model = policy.target_models[model]
twin_q = policy.config["twin_q"]
gamma = policy.config["gamma"]
n_step = policy.config["n_step"]
use_huber = policy.config["use_huber"]
huber_threshold = policy.config["huber_threshold"]
l2_reg = policy.config["l2_reg"]
input_dict = {
"obs": train_batch[SampleBatch.CUR_OBS],
"is_training": True,
}
input_dict_next = {
"obs": train_batch[SampleBatch.NEXT_OBS],
"is_training": True,
}
model_out_t, _ = model(input_dict, [], None)
model_out_tp1, _ = model(input_dict_next, [], None)
target_model_out_tp1, _ = target_model(input_dict_next, [], None)
# Policy network evaluation.
# prev_update_ops = set(tf1.get_collection(tf.GraphKeys.UPDATE_OPS))
policy_t = model.get_policy_output(model_out_t)
# policy_batchnorm_update_ops = list(
# set(tf1.get_collection(tf.GraphKeys.UPDATE_OPS)) - prev_update_ops)
policy_tp1 = target_model.get_policy_output(target_model_out_tp1)
# Action outputs.
if policy.config["smooth_target_policy"]:
target_noise_clip = policy.config["target_noise_clip"]
clipped_normal_sample = torch.clamp(
torch.normal(mean=torch.zeros(policy_tp1.size()),
std=policy.config["target_noise"]).to(
policy_tp1.device), -target_noise_clip,
target_noise_clip)
policy_tp1_smoothed = torch.min(
torch.max(
policy_tp1 + clipped_normal_sample,
torch.tensor(policy.action_space.low,
dtype=torch.float32,
device=policy_tp1.device)),
torch.tensor(policy.action_space.high,
dtype=torch.float32,
device=policy_tp1.device))
else:
# No smoothing, just use deterministic actions.
policy_tp1_smoothed = policy_tp1
# Q-net(s) evaluation.
# prev_update_ops = set(tf1.get_collection(tf.GraphKeys.UPDATE_OPS))
# Q-values for given actions & observations in given current
q_t = model.get_q_values(model_out_t, train_batch[SampleBatch.ACTIONS])
# Q-values for current policy (no noise) in given current state
q_t_det_policy = model.get_q_values(model_out_t, policy_t)
actor_loss = -torch.mean(q_t_det_policy)
if twin_q:
twin_q_t = model.get_twin_q_values(model_out_t,
train_batch[SampleBatch.ACTIONS])
# q_batchnorm_update_ops = list(
# set(tf1.get_collection(tf.GraphKeys.UPDATE_OPS)) - prev_update_ops)
# Target q-net(s) evaluation.
q_tp1 = target_model.get_q_values(target_model_out_tp1,
policy_tp1_smoothed)
if twin_q:
twin_q_tp1 = target_model.get_twin_q_values(target_model_out_tp1,
policy_tp1_smoothed)
q_t_selected = torch.squeeze(q_t, axis=len(q_t.shape) - 1)
if twin_q:
twin_q_t_selected = torch.squeeze(twin_q_t, axis=len(q_t.shape) - 1)
q_tp1 = torch.min(q_tp1, twin_q_tp1)
q_tp1_best = torch.squeeze(input=q_tp1, axis=len(q_tp1.shape) - 1)
q_tp1_best_masked = \
(1.0 - train_batch[SampleBatch.DONES].float()) * \
q_tp1_best
# Compute RHS of bellman equation.
q_t_selected_target = (train_batch[SampleBatch.REWARDS] +
gamma**n_step * q_tp1_best_masked).detach()
# Compute the error (potentially clipped).
if twin_q:
td_error = q_t_selected - q_t_selected_target
twin_td_error = twin_q_t_selected - q_t_selected_target
if use_huber:
errors = huber_loss(td_error, huber_threshold) \
+ huber_loss(twin_td_error, huber_threshold)
else:
errors = 0.5 * \
(torch.pow(td_error, 2.0) + torch.pow(twin_td_error, 2.0))
else:
td_error = q_t_selected - q_t_selected_target
if use_huber:
errors = huber_loss(td_error, huber_threshold)
else:
errors = 0.5 * torch.pow(td_error, 2.0)
critic_loss = torch.mean(train_batch[PRIO_WEIGHTS] * errors)
# Add l2-regularization if required.
if l2_reg is not None:
for name, var in model.policy_variables(as_dict=True).items():
if "bias" not in name:
actor_loss += (l2_reg * l2_loss(var))
for name, var in model.q_variables(as_dict=True).items():
if "bias" not in name:
critic_loss += (l2_reg * l2_loss(var))
# Model self-supervised losses.
if policy.config["use_state_preprocessor"]:
# Expand input_dict in case custom_loss' need them.
input_dict[SampleBatch.ACTIONS] = train_batch[SampleBatch.ACTIONS]
input_dict[SampleBatch.REWARDS] = train_batch[SampleBatch.REWARDS]
input_dict[SampleBatch.DONES] = train_batch[SampleBatch.DONES]
input_dict[SampleBatch.NEXT_OBS] = train_batch[SampleBatch.NEXT_OBS]
[actor_loss,
critic_loss] = model.custom_loss([actor_loss, critic_loss],
input_dict)
# Store values for stats function in model (tower), such that for
# multi-GPU, we do not override them during the parallel loss phase.
model.tower_stats["q_t"] = q_t
model.tower_stats["actor_loss"] = actor_loss
model.tower_stats["critic_loss"] = critic_loss
# TD-error tensor in final stats
# will be concatenated and retrieved for each individual batch item.
model.tower_stats["td_error"] = td_error
# Return two loss terms (corresponding to the two optimizers, we create).
return actor_loss, critic_loss
def actor_critic_loss(
policy: Policy, model: ModelV2,
dist_class: Type[TorchDistributionWrapper],
train_batch: SampleBatch) -> Union[TensorType, List[TensorType]]:
"""Constructs the loss for the Soft Actor Critic.
Args:
policy (Policy): The Policy to calculate the loss for.
model (ModelV2): The Model to calculate the loss for.
dist_class (Type[TorchDistributionWrapper]: The action distr. class.
train_batch (SampleBatch): The training data.
Returns:
Union[TensorType, List[TensorType]]: A single loss tensor or a list
of loss tensors.
"""
# Look up the target model (tower) using the model tower.
target_model = policy.target_models[model]
# Should be True only for debugging purposes (e.g. test cases)!
deterministic = policy.config["_deterministic_loss"]
model_out_t, _ = model(
{
"obs": train_batch[SampleBatch.CUR_OBS],
"is_training": True,
}, [], None)
model_out_tp1, _ = model(
{
"obs": train_batch[SampleBatch.NEXT_OBS],
"is_training": True,
}, [], None)
target_model_out_tp1, _ = target_model(
{
"obs": train_batch[SampleBatch.NEXT_OBS],
"is_training": True,
}, [], None)
alpha = torch.exp(model.log_alpha)
# Discrete case.
if model.discrete:
# Get all action probs directly from pi and form their logp.
log_pis_t = F.log_softmax(model.get_policy_output(model_out_t), dim=-1)
policy_t = torch.exp(log_pis_t)
log_pis_tp1 = F.log_softmax(model.get_policy_output(model_out_tp1), -1)
policy_tp1 = torch.exp(log_pis_tp1)
# Q-values.
q_t = model.get_q_values(model_out_t)
# Target Q-values.
q_tp1 = target_model.get_q_values(target_model_out_tp1)
if policy.config["twin_q"]:
twin_q_t = model.get_twin_q_values(model_out_t)
twin_q_tp1 = target_model.get_twin_q_values(target_model_out_tp1)
q_tp1 = torch.min(q_tp1, twin_q_tp1)
q_tp1 -= alpha * log_pis_tp1
# Actually selected Q-values (from the actions batch).
one_hot = F.one_hot(train_batch[SampleBatch.ACTIONS].long(),
num_classes=q_t.size()[-1])
q_t_selected = torch.sum(q_t * one_hot, dim=-1)
if policy.config["twin_q"]:
twin_q_t_selected = torch.sum(twin_q_t * one_hot, dim=-1)
# Discrete case: "Best" means weighted by the policy (prob) outputs.
q_tp1_best = torch.sum(torch.mul(policy_tp1, q_tp1), dim=-1)
q_tp1_best_masked = \
(1.0 - train_batch[SampleBatch.DONES].float()) * \
q_tp1_best
# Continuous actions case.
else:
# Sample single actions from distribution.
action_dist_class = _get_dist_class(policy, policy.config,
policy.action_space)
action_dist_t = action_dist_class(model.get_policy_output(model_out_t),
model)
policy_t = action_dist_t.sample() if not deterministic else \
action_dist_t.deterministic_sample()
log_pis_t = torch.unsqueeze(action_dist_t.logp(policy_t), -1)
action_dist_tp1 = action_dist_class(
model.get_policy_output(model_out_tp1), model)
policy_tp1 = action_dist_tp1.sample() if not deterministic else \
action_dist_tp1.deterministic_sample()
log_pis_tp1 = torch.unsqueeze(action_dist_tp1.logp(policy_tp1), -1)
# Q-values for the actually selected actions.
q_t = model.get_q_values(model_out_t, train_batch[SampleBatch.ACTIONS])
if policy.config["twin_q"]:
twin_q_t = model.get_twin_q_values(
model_out_t, train_batch[SampleBatch.ACTIONS])
# Q-values for current policy in given current state.
q_t_det_policy = model.get_q_values(model_out_t, policy_t)
if policy.config["twin_q"]:
twin_q_t_det_policy = model.get_twin_q_values(
model_out_t, policy_t)
q_t_det_policy = torch.min(q_t_det_policy, twin_q_t_det_policy)
# Target q network evaluation.
q_tp1 = target_model.get_q_values(target_model_out_tp1, policy_tp1)
if policy.config["twin_q"]:
twin_q_tp1 = target_model.get_twin_q_values(
target_model_out_tp1, policy_tp1)
# Take min over both twin-NNs.
q_tp1 = torch.min(q_tp1, twin_q_tp1)
q_t_selected = torch.squeeze(q_t, dim=-1)
if policy.config["twin_q"]:
twin_q_t_selected = torch.squeeze(twin_q_t, dim=-1)
q_tp1 -= alpha * log_pis_tp1
q_tp1_best = torch.squeeze(input=q_tp1, dim=-1)
q_tp1_best_masked = (1.0 - train_batch[SampleBatch.DONES].float()) * \
q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = (train_batch[SampleBatch.REWARDS] +
(policy.config["gamma"]**policy.config["n_step"]) *
q_tp1_best_masked).detach()
# Compute the TD-error (potentially clipped).
base_td_error = torch.abs(q_t_selected - q_t_selected_target)
if policy.config["twin_q"]:
twin_td_error = torch.abs(twin_q_t_selected - q_t_selected_target)
td_error = 0.5 * (base_td_error + twin_td_error)
else:
td_error = base_td_error
critic_loss = [
torch.mean(train_batch[PRIO_WEIGHTS] * huber_loss(base_td_error))
]
if policy.config["twin_q"]:
critic_loss.append(
torch.mean(train_batch[PRIO_WEIGHTS] * huber_loss(twin_td_error)))
# Alpha- and actor losses.
# Note: In the papers, alpha is used directly, here we take the log.
# Discrete case: Multiply the action probs as weights with the original
# loss terms (no expectations needed).
if model.discrete:
weighted_log_alpha_loss = policy_t.detach() * (
-model.log_alpha * (log_pis_t + model.target_entropy).detach())
# Sum up weighted terms and mean over all batch items.
alpha_loss = torch.mean(torch.sum(weighted_log_alpha_loss, dim=-1))
# Actor loss.
actor_loss = torch.mean(
torch.sum(
torch.mul(
# NOTE: No stop_grad around policy output here
# (compare with q_t_det_policy for continuous case).
policy_t,
alpha.detach() * log_pis_t - q_t.detach()),
dim=-1))
else:
alpha_loss = -torch.mean(model.log_alpha *
(log_pis_t + model.target_entropy).detach())
# Note: Do not detach q_t_det_policy here b/c is depends partly
# on the policy vars (policy sample pushed through Q-net).
# However, we must make sure `actor_loss` is not used to update
# the Q-net(s)' variables.
actor_loss = torch.mean(alpha.detach() * log_pis_t - q_t_det_policy)
# Store values for stats function in model (tower), such that for
# multi-GPU, we do not override them during the parallel loss phase.
model.tower_stats["q_t"] = q_t
model.tower_stats["policy_t"] = policy_t
model.tower_stats["log_pis_t"] = log_pis_t
model.tower_stats["actor_loss"] = actor_loss
model.tower_stats["critic_loss"] = critic_loss
model.tower_stats["alpha_loss"] = alpha_loss
# TD-error tensor in final stats
# will be concatenated and retrieved for each individual batch item.
model.tower_stats["td_error"] = td_error
# Return all loss terms corresponding to our optimizers.
return tuple([actor_loss] + critic_loss + [alpha_loss])
def ddpg_actor_critic_loss(policy, model, _, train_batch):
twin_q = policy.config["twin_q"]
gamma = policy.config["gamma"]
n_step = policy.config["n_step"]
use_huber = policy.config["use_huber"]
huber_threshold = policy.config["huber_threshold"]
l2_reg = policy.config["l2_reg"]
input_dict = {
"obs": train_batch[SampleBatch.CUR_OBS],
"is_training": True,
}
input_dict_next = {
"obs": train_batch[SampleBatch.NEXT_OBS],
"is_training": True,
}
model_out_t, _ = model(input_dict, [], None)
model_out_tp1, _ = model(input_dict_next, [], None)
target_model_out_tp1, _ = policy.target_model(input_dict_next, [], None)
# Policy network evaluation.
# prev_update_ops = set(tf.get_collection(tf.GraphKeys.UPDATE_OPS))
policy_t = model.get_policy_output(model_out_t)
# policy_batchnorm_update_ops = list(
# set(tf.get_collection(tf.GraphKeys.UPDATE_OPS)) - prev_update_ops)
policy_tp1 = \
policy.target_model.get_policy_output(target_model_out_tp1)
# Action outputs.
if policy.config["smooth_target_policy"]:
target_noise_clip = policy.config["target_noise_clip"]
clipped_normal_sample = torch.clamp(
torch.normal(mean=torch.zeros(policy_tp1.size()),
std=policy.config["target_noise"]),
-target_noise_clip, target_noise_clip)
policy_tp1_smoothed = torch.clamp(policy_tp1 + clipped_normal_sample,
policy.action_space.low.item(0),
policy.action_space.high.item(0))
else:
# No smoothing, just use deterministic actions.
policy_tp1_smoothed = policy_tp1
# Q-net(s) evaluation.
# prev_update_ops = set(tf.get_collection(tf.GraphKeys.UPDATE_OPS))
# Q-values for given actions & observations in given current
q_t = model.get_q_values(model_out_t, train_batch[SampleBatch.ACTIONS])
# Q-values for current policy (no noise) in given current state
q_t_det_policy = model.get_q_values(model_out_t, policy_t)
if twin_q:
twin_q_t = model.get_twin_q_values(model_out_t,
train_batch[SampleBatch.ACTIONS])
# q_batchnorm_update_ops = list(
# set(tf.get_collection(tf.GraphKeys.UPDATE_OPS)) - prev_update_ops)
# Target q-net(s) evaluation.
q_tp1 = policy.target_model.get_q_values(target_model_out_tp1,
policy_tp1_smoothed)
if twin_q:
twin_q_tp1 = policy.target_model.get_twin_q_values(
target_model_out_tp1, policy_tp1_smoothed)
q_t_selected = torch.squeeze(q_t, axis=len(q_t.shape) - 1)
if twin_q:
twin_q_t_selected = torch.squeeze(twin_q_t, axis=len(q_t.shape) - 1)
q_tp1 = torch.min(q_tp1, twin_q_tp1)
q_tp1_best = torch.squeeze(input=q_tp1, axis=len(q_tp1.shape) - 1)
q_tp1_best_masked = \
(1.0 - train_batch[SampleBatch.DONES].float()) * \
q_tp1_best
# Compute RHS of bellman equation.
q_t_selected_target = (train_batch[SampleBatch.REWARDS] +
gamma**n_step * q_tp1_best_masked).detach()
# Compute the error (potentially clipped).
if twin_q:
td_error = q_t_selected - q_t_selected_target
twin_td_error = twin_q_t_selected - q_t_selected_target
td_error = td_error + twin_td_error
if use_huber:
errors = huber_loss(td_error, huber_threshold) \
+ huber_loss(twin_td_error, huber_threshold)
else:
errors = 0.5 * \
(torch.pow(td_error, 2.0) + torch.pow(twin_td_error, 2.0))
else:
td_error = q_t_selected - q_t_selected_target
if use_huber:
errors = huber_loss(td_error, huber_threshold)
else:
errors = 0.5 * torch.pow(td_error, 2.0)
critic_loss = torch.mean(train_batch[PRIO_WEIGHTS] * errors)
actor_loss = -torch.mean(q_t_det_policy)
# Add l2-regularization if required.
if l2_reg is not None:
for name, var in policy.model.policy_variables(as_dict=True).items():
if "bias" not in name:
actor_loss += (l2_reg * l2_loss(var))
for name, var in policy.model.q_variables(as_dict=True).items():
if "bias" not in name:
critic_loss += (l2_reg * l2_loss(var))
# Model self-supervised losses.
if policy.config["use_state_preprocessor"]:
# Expand input_dict in case custom_loss' need them.
input_dict[SampleBatch.ACTIONS] = train_batch[SampleBatch.ACTIONS]
input_dict[SampleBatch.REWARDS] = train_batch[SampleBatch.REWARDS]
input_dict[SampleBatch.DONES] = train_batch[SampleBatch.DONES]
input_dict[SampleBatch.NEXT_OBS] = train_batch[SampleBatch.NEXT_OBS]
[actor_loss,
critic_loss] = model.custom_loss([actor_loss, critic_loss],
input_dict)
# Store values for stats function.
policy.actor_loss = actor_loss
policy.critic_loss = critic_loss
policy.td_error = td_error
policy.q_t = q_t
# Return one loss value (even though we treat them separately in our
# 2 optimizers: actor and critic).
return policy.actor_loss, policy.critic_loss
def __init__(self,
q_t_selected,
q_logits_t_selected, # for distributional
q_tp1_best,
q_dist_tp1_best, # for distributional
importance_weights,
rewards,
done_mask,
gamma=0.99,
n_step=1,
num_atoms=1,
v_min=-10.0,
v_max=10.0):
if num_atoms > 1:
# # NOTE: LAZY DEV
# raise ValueError("Torch version of DQN does not support "
# "distributional Q yet!")
# Distributional Q-learning which corresponds to an entropy loss
z = torch.arange(num_atoms).float().to(q_dist_tp1_best.device)
z = v_min + z * (v_max - v_min) / float(num_atoms - 1)
# (batch_size, 1) * (1, num_atoms) = (batch_size, num_atoms)
r_tau = torch.unsqueeze(rewards, -1) + gamma**n_step * torch.unsqueeze(
1.0 - done_mask, -1) * torch.unsqueeze(z, 0)
r_tau = torch.clamp(r_tau, v_min, v_max)
b = (r_tau - v_min) / ((v_max - v_min) / float(num_atoms - 1))
lb = torch.floor(b)
ub = torch.ceil(b)
# indispensable judgement which is missed in most implementations
# when b happens to be an integer, lb == ub, so pr_j(s', a*) will
# be discarded because (ub-b) == (b-lb) == 0
floor_equal_ceil = torch.le(ub - lb, 0.5).float()
# (batch_size, num_atoms, num_atoms)
l_project = F.one_hot(lb.long(), num_atoms)
# (batch_size, num_atoms, num_atoms)
u_project = F.one_hot(ub.long(), num_atoms)
ml_delta = q_dist_tp1_best * (ub - b + floor_equal_ceil)
mu_delta = q_dist_tp1_best * (b - lb)
ml_delta = torch.sum(
l_project * torch.unsqueeze(ml_delta, -1), dim=1)
mu_delta = torch.sum(
u_project * torch.unsqueeze(mu_delta, -1), dim=1)
m = ml_delta + mu_delta
# Rainbow paper claims that using this cross entropy loss for
# priority is robust and insensitive to `prioritized_replay_alpha`
# self.td_error = tf.nn.softmax_cross_entropy_with_logits(
# labels=m, logits=q_logits_t_selected)
# pytorch equivalent to tf.nn.softmax_cross_entropy_with_logits
# https://gist.github.com/tejaskhot/cf3d087ce4708c422e68b3b747494b9f
self.td_error = -m * F.log_softmax(q_logits_t_selected, -1)
self.loss = torch.mean(
self.td_error * importance_weights.float())
self.stats = {
# TODO: better Q stats for dist dqn
"mean_td_error": tf.reduce_mean(self.td_error),
}
else:
q_tp1_best_masked = (1.0 - done_mask) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = rewards + gamma**n_step * q_tp1_best_masked
# compute the error (potentially clipped)
self.td_error = q_t_selected - q_t_selected_target.detach()
self.loss = torch.mean(
importance_weights.float() * huber_loss(self.td_error))
self.stats = {
"mean_q": torch.mean(q_t_selected),
"min_q": torch.min(q_t_selected),
"max_q": torch.max(q_t_selected),
"td_error": self.td_error,
"mean_td_error": torch.mean(self.td_error),
}
