def process_fn(self, batch: Batch, buffer: ReplayBuffer,
indice: np.ndarray) -> Batch:
if self._rew_norm:
mean, std = batch.rew.mean(), batch.rew.std()
if not np.isclose(std, 0, 1e-2):
batch.rew = (batch.rew - mean) / std
v, v_, old_log_prob = [], [], []
with torch.no_grad():
for b in batch.split(self._batch, shuffle=False):
v_.append(self.critic(b.obs_next))
v.append(self.critic(b.obs))
old_log_prob.append(self(b).dist.log_prob(
to_torch_as(b.act, v[0])))
v_ = to_numpy(torch.cat(v_, dim=0))
batch = self.compute_episodic_return(
batch, v_, gamma=self._gamma, gae_lambda=self._lambda,
rew_norm=self._rew_norm)
batch.v = torch.cat(v, dim=0).flatten() # old value
batch.act = to_torch_as(batch.act, v[0])
batch.logp_old = torch.cat(old_log_prob, dim=0)
batch.returns = to_torch_as(batch.returns, v[0])
batch.adv = batch.returns - batch.v
if self._rew_norm:
mean, std = batch.adv.mean(), batch.adv.std()
if not np.isclose(std.item(), 0, 1e-2):
batch.adv = (batch.adv - mean) / std
return batch
def learn( # type: ignore
self, batch: Batch, batch_size: int, repeat: int,
**kwargs: Any) -> Dict[str, List[float]]:
losses, actor_losses, vf_losses, ent_losses = [], [], [], []
for _ in range(repeat):
for b in batch.split(batch_size, merge_last=True):
self.optim.zero_grad()
dist = self(b).dist
v = self.critic(b.obs).flatten()
a = to_torch_as(b.act, v)
r = to_torch_as(b.returns, v)
log_prob = dist.log_prob(a).reshape(len(r), -1).transpose(0, 1)
a_loss = -(log_prob * (r - v).detach()).mean()
vf_loss = F.mse_loss(r, v) # type: ignore
ent_loss = dist.entropy().mean()
loss = a_loss + self._w_vf * vf_loss - self._w_ent * ent_loss
loss.backward()
if self._grad_norm is not None:
nn.utils.clip_grad_norm_(
list(self.actor.parameters()) +
list(self.critic.parameters()),
max_norm=self._grad_norm,
)
self.optim.step()
actor_losses.append(a_loss.item())
vf_losses.append(vf_loss.item())
ent_losses.append(ent_loss.item())
losses.append(loss.item())
return {
"loss": losses,
"loss/actor": actor_losses,
"loss/vf": vf_losses,
"loss/ent": ent_losses,
}
def learn(self, batch: Batch, batch_size: int, repeat: int,
**kwargs) -> Dict[str, List[float]]:
self._batch = batch_size
r = batch.returns
if self._rew_norm and not np.isclose(r.std(), 0):
batch.returns = (r - r.mean()) / r.std()
losses, actor_losses, vf_losses, ent_losses = [], [], [], []
for _ in range(repeat):
for b in batch.split(batch_size):
self.optim.zero_grad()
dist = self(b).dist
v = self.critic(b.obs).squeeze(-1)
a = to_torch_as(b.act, v)
r = to_torch_as(b.returns, v)
a_loss = -(dist.log_prob(a).reshape(v.shape) *
(r - v).detach()).mean()
vf_loss = F.mse_loss(r, v)
ent_loss = dist.entropy().mean()
loss = a_loss + self._w_vf * vf_loss - self._w_ent * ent_loss
loss.backward()
if self._grad_norm is not None:
nn.utils.clip_grad_norm_(list(self.actor.parameters()) +
list(self.critic.parameters()),
max_norm=self._grad_norm)
self.optim.step()
actor_losses.append(a_loss.item())
vf_losses.append(vf_loss.item())
ent_losses.append(ent_loss.item())
losses.append(loss.item())
return {
'loss': losses,
'loss/actor': actor_losses,
'loss/vf': vf_losses,
'loss/ent': ent_losses,
}
def _compute_returns(self, batch: Batch, buffer: ReplayBuffer,
indice: np.ndarray) -> Batch:
v_s, v_s_ = [], []
with torch.no_grad():
for b in batch.split(self._batch, shuffle=False, merge_last=True):
v_s.append(self.critic(b.obs))
v_s_.append(self.critic(b.obs_next))
batch.v_s = torch.cat(v_s, dim=0).flatten() # old value
v_s = batch.v_s.cpu().numpy()
v_s_ = torch.cat(v_s_, dim=0).flatten().cpu().numpy()
# when normalizing values, we do not minus self.ret_rms.mean to be numerically
# consistent with OPENAI baselines' value normalization pipeline. Emperical
# study also shows that "minus mean" will harm performances a tiny little bit
# due to unknown reasons (on Mujoco envs, not confident, though).
if self._rew_norm: # unnormalize v_s & v_s_
v_s = v_s * np.sqrt(self.ret_rms.var + self._eps)
v_s_ = v_s_ * np.sqrt(self.ret_rms.var + self._eps)
unnormalized_returns, advantages = self.compute_episodic_return(
batch,
buffer,
indice,
v_s_,
v_s,
gamma=self._gamma,
gae_lambda=self._lambda)
if self._rew_norm:
batch.returns = unnormalized_returns / \
np.sqrt(self.ret_rms.var + self._eps)
self.ret_rms.update(unnormalized_returns)
else:
batch.returns = unnormalized_returns
batch.returns = to_torch_as(batch.returns, batch.v_s)
batch.adv = to_torch_as(advantages, batch.v_s)
return batch
def process_fn(
self, batch: Batch, buffer: ReplayBuffer, indice: np.ndarray
) -> Batch:
v_s, v_s_, old_log_prob = [], [], []
with torch.no_grad():
for b in batch.split(self._batch, shuffle=False, merge_last=True):
v_s.append(self.critic(b.obs))
v_s_.append(self.critic(b.obs_next))
old_log_prob.append(self(b).dist.log_prob(to_torch_as(b.act, v_s[0])))
batch.v_s = torch.cat(v_s, dim=0).flatten() # old value
v_s = to_numpy(batch.v_s)
v_s_ = to_numpy(torch.cat(v_s_, dim=0).flatten())
if self._rew_norm: # unnormalize v_s & v_s_
v_s = v_s * np.sqrt(self.ret_rms.var + self._eps) + self.ret_rms.mean
v_s_ = v_s_ * np.sqrt(self.ret_rms.var + self._eps) + self.ret_rms.mean
unnormalized_returns, advantages = self.compute_episodic_return(
batch, buffer, indice, v_s_, v_s,
gamma=self._gamma, gae_lambda=self._lambda)
if self._rew_norm:
batch.returns = (unnormalized_returns - self.ret_rms.mean) / \
np.sqrt(self.ret_rms.var + self._eps)
self.ret_rms.update(unnormalized_returns)
mean, std = np.mean(advantages), np.std(advantages)
advantages = (advantages - mean) / std # per-batch norm
else:
batch.returns = unnormalized_returns
batch.act = to_torch_as(batch.act, batch.v_s)
batch.logp_old = torch.cat(old_log_prob, dim=0)
batch.returns = to_torch_as(batch.returns, batch.v_s)
batch.adv = to_torch_as(advantages, batch.v_s)
return batch
def compute_nstep_return(
batch: Batch,
buffer: ReplayBuffer,
indice: np.ndarray,
target_q_fn: Callable[[ReplayBuffer, np.ndarray], torch.Tensor],
gamma: float = 0.99,
n_step: int = 1,
rew_norm: bool = False,
) -> Batch:
r"""Compute n-step return for Q-learning targets.
.. math::
G_t = \sum_{i = t}^{t + n - 1} \gamma^{i - t}(1 - d_i)r_i +
\gamma^n (1 - d_{t + n}) Q_{\mathrm{target}}(s_{t + n})
where :math:`\gamma` is the discount factor,
:math:`\gamma \in [0, 1]`, :math:`d_t` is the done flag of step
:math:`t`.
:param batch: a data batch, which is equal to buffer[indice].
:type batch: :class:`~tianshou.data.Batch`
:param buffer: a data buffer which contains several full-episode data
chronologically.
:type buffer: :class:`~tianshou.data.ReplayBuffer`
:param indice: sampled timestep.
:type indice: numpy.ndarray
:param function target_q_fn: a function receives :math:`t+n-1` step's
data and compute target Q value.
:param float gamma: the discount factor, should be in [0, 1], defaults
to 0.99.
:param int n_step: the number of estimation step, should be an int
greater than 0, defaults to 1.
:param bool rew_norm: normalize the reward to Normal(0, 1), defaults
to False.
:return: a Batch. The result will be stored in batch.returns as a
torch.Tensor with shape (bsz, ).
"""
rew = buffer.rew
if rew_norm:
bfr = rew[:min(len(buffer), 1000)] # avoid large buffer
mean, std = bfr.mean(), bfr.std()
if np.isclose(std, 0, 1e-2):
mean, std = 0.0, 1.0
else:
mean, std = 0.0, 1.0
buf_len = len(buffer)
terminal = (indice + n_step - 1) % buf_len
target_q_torch = target_q_fn(buffer, terminal).flatten() # (bsz, )
target_q = to_numpy(target_q_torch)
target_q = _nstep_return(rew, buffer.done, target_q, indice, gamma,
n_step, len(buffer), mean, std)
batch.returns = to_torch_as(target_q, target_q_torch)
# prio buffer update
if isinstance(buffer, PrioritizedReplayBuffer):
batch.weight = to_torch_as(batch.weight, target_q_torch)
return batch
def compute_nstep_return(batch: Batch,
buffer: ReplayBuffer,
indice: np.ndarray,
target_q_fn: Callable[[ReplayBuffer, np.ndarray],
torch.Tensor],
gamma: float = 0.99,
n_step: int = 1,
rew_norm: bool = False) -> np.ndarray:
r"""Compute n-step return for Q-learning targets:
.. math::
G_t = \sum_{i = t}^{t + n - 1} \gamma^{i - t}(1 - d_i)r_i +
\gamma^n (1 - d_{t + n}) Q_{\mathrm{target}}(s_{t + n})
, where :math:`\gamma` is the discount factor,
:math:`\gamma \in [0, 1]`, :math:`d_t` is the done flag of step
:math:`t`.
:param batch: a data batch, which is equal to buffer[indice].
:type batch: :class:`~tianshou.data.Batch`
:param buffer: a data buffer which contains several full-episode data
chronologically.
:type buffer: :class:`~tianshou.data.ReplayBuffer`
:param indice: sampled timestep.
:type indice: numpy.ndarray
:param function target_q_fn: a function receives :math:`t+n-1` step's
data and compute target Q value.
:param float gamma: the discount factor, should be in [0, 1], defaults
to 0.99.
:param int n_step: the number of estimation step, should be an int
greater than 0, defaults to 1.
:param bool rew_norm: normalize the reward to Normal(0, 1), defaults
to ``False``.
:return: a Batch. The result will be stored in batch.returns as a
torch.Tensor with shape (bsz, ).
"""
if rew_norm:
bfr = buffer.rew[:min(len(buffer), 1000)] # avoid large buffer
mean, std = bfr.mean(), bfr.std()
if np.isclose(std, 0):
mean, std = 0, 1
else:
mean, std = 0, 1
returns = np.zeros_like(indice)
gammas = np.zeros_like(indice) + n_step
done, rew, buf_len = buffer.done, buffer.rew, len(buffer)
for n in range(n_step - 1, -1, -1):
now = (indice + n) % buf_len
gammas[done[now] > 0] = n
returns[done[now] > 0] = 0
returns = (rew[now] - mean) / std + gamma * returns
terminal = (indice + n_step - 1) % buf_len
target_q = target_q_fn(buffer, terminal).squeeze()
target_q[gammas != n_step] = 0
returns = to_torch_as(returns, target_q)
gammas = to_torch_as(gamma**gammas, target_q)
batch.returns = target_q * gammas + returns
return batch
def compute_nstep_return(
batch: Batch,
buffer: ReplayBuffer,
indice: np.ndarray,
target_q_fn: Callable[[ReplayBuffer, np.ndarray], torch.Tensor],
gamma: float = 0.99,
n_step: int = 1,
rew_norm: bool = False,
use_mixed: bool = False,
) -> Batch:
r"""Compute n-step return for Q-learning targets.
.. math::
G_t = \sum_{i = t}^{t + n - 1} \gamma^{i - t}(1 - d_i)r_i +
\gamma^n (1 - d_{t + n}) Q_{\mathrm{target}}(s_{t + n})
where :math:`\gamma` is the discount factor, :math:`\gamma \in [0, 1]`,
:math:`d_t` is the done flag of step :math:`t`.
:param Batch batch: a data batch, which is equal to buffer[indice].
:param ReplayBuffer buffer: the data buffer.
:param function target_q_fn: a function which compute target Q value
of "obs_next" given data buffer and wanted indices.
:param float gamma: the discount factor, should be in [0, 1]. Default to 0.99.
:param int n_step: the number of estimation step, should be an int greater
than 0. Default to 1.
:param bool rew_norm: normalize the reward to Normal(0, 1), Default to False.
:return: a Batch. The result will be stored in batch.returns as a
torch.Tensor with the same shape as target_q_fn's return tensor.
"""
assert not rew_norm, \
"Reward normalization in computing n-step returns is unsupported now."
rew = buffer.rew
bsz = len(indice)
indices = [indice]
for _ in range(n_step - 1):
indices.append(buffer.next(indices[-1]))
indices = np.stack(indices)
# terminal indicates buffer indexes nstep after 'indice',
# and are truncated at the end of each episode
terminal = indices[-1]
with autocast(enabled=use_mixed):
with torch.no_grad():
target_q_torch = target_q_fn(buffer, terminal) # (bsz, ?)
target_q = to_numpy(target_q_torch.float().reshape(bsz, -1))
target_q = target_q * BasePolicy.value_mask(buffer, terminal).reshape(
-1, 1)
end_flag = buffer.done.copy()
end_flag[buffer.unfinished_index()] = True
target_q = _nstep_return(rew, end_flag, target_q, indices, gamma,
n_step)
batch.returns = to_torch_as(target_q, target_q_torch)
if hasattr(batch, "weight"): # prio buffer update
batch.weight = to_torch_as(batch.weight, target_q_torch)
return batch
def learn( # type: ignore
self, batch: Batch, batch_size: int, repeat: int,
**kwargs: Any) -> Dict[str, List[float]]:
losses = []
for _ in range(repeat):
for b in batch.split(batch_size, merge_last=True):
self.optim.zero_grad()
dist = self(b).dist
a = to_torch_as(b.act, dist.logits)
r = to_torch_as(b.returns, dist.logits)
log_prob = dist.log_prob(a).reshape(len(r), -1).transpose(0, 1)
loss = -(log_prob * r).mean()
loss.backward()
self.optim.step()
losses.append(loss.item())
return {"loss": losses}
def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
if self._target and self._iter % self._freq == 0:
self.sync_weight()
self.optim.zero_grad()
weight = batch.pop("weight", 1.0)
q = self(batch).logits
q = q[np.arange(len(q)), batch.act]
r = to_torch_as(batch.returns.flatten(), q)
weight = to_torch_as(weight, q)
td = r - q
loss = (td.pow(2) * weight).mean()
batch.weight = td # prio-buffer
loss.backward()
self.optim.step()
self._iter += 1
return {"loss": loss.item()}
def forward(self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
model: str = 'actor',
input: str = 'obs',
explorating: bool = True,
**kwargs) -> Batch:
"""Compute action over the given batch data.
:return: A :class:`~tianshou.data.Batch` which has 2 keys:
* ``act`` the action.
* ``state`` the hidden state.
.. seealso::
Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
more detailed explanation.
"""
model = getattr(self, model)
obs = getattr(batch, input)
logits, h = model(obs, state=state, info=batch.info)
actions = torch.tanh(logits)
if self.training and explorating:
actions = actions + to_torch_as(self._noise(actions.shape),
actions)
actions = actions.clamp(self._range[0], self._range[1])
return Batch(act=actions, state=h)
def forward( # type: ignore
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
input: str = "obs",
**kwargs: Any,
) -> Batch:
obs = batch[input]
logits, h = self.actor(obs, state=state, info=batch.info)
assert isinstance(logits, tuple)
dist = Independent(Normal(*logits), 1)
if self._deterministic_eval and not self.training:
act = logits[0]
else:
act = dist.rsample()
log_prob = dist.log_prob(act).unsqueeze(-1)
# apply correction for Tanh squashing when computing logprob from Gaussian
# You can check out the original SAC paper (arXiv 1801.01290): Eq 21.
# in appendix C to get some understanding of this equation.
if self.action_scaling and self.action_space is not None:
action_scale = to_torch_as(
(self.action_space.high - self.action_space.low) / 2.0, act)
else:
action_scale = 1.0 # type: ignore
squashed_action = torch.tanh(act)
log_prob = log_prob - torch.log(action_scale *
(1 - squashed_action.pow(2)) +
self.__eps).sum(-1, keepdim=True)
return Batch(logits=logits,
act=squashed_action,
state=h,
dist=dist,
log_prob=log_prob)
def forward(
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
model: str = "actor",
input: str = "obs",
**kwargs: Any,
) -> Batch:
"""Compute action over the given batch data.
:return: A :class:`~tianshou.data.Batch` which has 2 keys:
* ``act`` the action.
* ``state`` the hidden state.
.. seealso::
Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
more detailed explanation.
"""
model = getattr(self, model)
obs = batch[input]
actions, h = model(obs, state=state, info=batch.info)
actions += self._action_bias
if self._noise and not self.updating:
actions += to_torch_as(self._noise(actions.shape), actions)
actions = actions.clamp(self._range[0], self._range[1])
return Batch(act=actions, state=h)
def learn(self, batch: Batch, **kwargs) -> Dict[str, float]:
# critic 1
current_q1 = self.critic1(batch.obs, batch.act)
target_q = to_torch_as(batch.returns, current_q1)[:, None]
critic1_loss = F.mse_loss(current_q1, target_q)
self.critic1_optim.zero_grad()
critic1_loss.backward()
self.critic1_optim.step()
# critic 2
current_q2 = self.critic2(batch.obs, batch.act)
critic2_loss = F.mse_loss(current_q2, target_q)
self.critic2_optim.zero_grad()
critic2_loss.backward()
self.critic2_optim.step()
# actor
obs_result = self(batch)
a = obs_result.act
current_q1a = self.critic1(batch.obs, a)
current_q2a = self.critic2(batch.obs, a)
actor_loss = (self._alpha * obs_result.log_prob -
torch.min(current_q1a, current_q2a)).mean()
self.actor_optim.zero_grad()
actor_loss.backward()
self.actor_optim.step()
self.sync_weight()
return {
'loss/actor': actor_loss.item(),
'loss/critic1': critic1_loss.item(),
'loss/critic2': critic2_loss.item(),
}
def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
# critic 1&2
td1, critic1_loss = self._mse_optimizer(
batch, self.critic1, self.critic1_optim
)
td2, critic2_loss = self._mse_optimizer(
batch, self.critic2, self.critic2_optim
)
batch.weight = (td1 + td2) / 2.0 # prio-buffer
# actor
if self._cnt % self._freq == 0:
act = self(batch, eps=0.0).act
q_value = self.critic1(batch.obs, act)
lmbda = self._alpha / q_value.abs().mean().detach()
actor_loss = -lmbda * q_value.mean() + F.mse_loss(
act, to_torch_as(batch.act, act)
)
self.actor_optim.zero_grad()
actor_loss.backward()
self._last = actor_loss.item()
self.actor_optim.step()
self.sync_weight()
self._cnt += 1
return {
"loss/actor": self._last,
"loss/critic1": critic1_loss.item(),
"loss/critic2": critic2_loss.item(),
}
def forward( # type: ignore
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
input: str = "obs",
**kwargs: Any,
) -> Batch:
obs = batch[input]
logits, h = self.actor(obs, state=state, info=batch.info)
assert isinstance(logits, tuple)
dist = Independent(Normal(*logits), 1)
if self._deterministic_eval and not self.training:
x = logits[0]
else:
x = dist.rsample()
y = torch.tanh(x)
act = y * self._action_scale + self._action_bias
y = self._action_scale * (1 - y.pow(2)) + self.__eps
log_prob = dist.log_prob(x).unsqueeze(-1)
log_prob = log_prob - torch.log(y).sum(-1, keepdim=True)
if self._noise is not None and self.training and not self.updating:
act += to_torch_as(self._noise(act.shape), act)
act = act.clamp(self._range[0], self._range[1])
return Batch(
logits=logits, act=act, state=h, dist=dist, log_prob=log_prob)
def compute_q_value(self, logits: torch.Tensor,
mask: Optional[np.ndarray]) -> torch.Tensor:
"""Compute the q value based on the network's raw output and action mask."""
if mask is not None:
# the masked q value should be smaller than logits.min()
min_value = logits.min() - logits.max() - 1.0
logits = logits + to_torch_as(1 - mask, logits) * min_value
return logits
def learn(self, batch: Batch, batch_size: int, repeat: int,
**kwargs) -> Dict[str, List[float]]:
losses = []
r = batch.returns
if self._rew_norm and not np.isclose(r.std(), 0):
batch.returns = (r - r.mean()) / r.std()
for _ in range(repeat):
for b in batch.split(batch_size):
self.optim.zero_grad()
dist = self(b).dist
a = to_torch_as(b.act, dist.logits)
r = to_torch_as(b.returns, dist.logits)
loss = -(dist.log_prob(a) * r).sum()
loss.backward()
self.optim.step()
losses.append(loss.item())
return {'loss': losses}
def learn(self, batch: Batch, **kwargs) -> Dict[str, float]:
if self._target and self._cnt % self._freq == 0:
self.sync_weight()
self.optim.zero_grad()
q = self(batch).logits
q = q[np.arange(len(q)), batch.act]
r = to_torch_as(batch.returns, q)
if hasattr(batch, 'update_weight'):
td = r - q
batch.update_weight(batch.indice, to_numpy(td))
impt_weight = to_torch_as(batch.impt_weight, q)
loss = (td.pow(2) * impt_weight).mean()
else:
loss = F.mse_loss(q, r)
loss.backward()
self.optim.step()
self._cnt += 1
return {'loss': loss.item()}
def _target_q(self, buffer: ReplayBuffer,
indice: np.ndarray) -> torch.Tensor:
batch = buffer[indice] # batch.obs: s_{t+n}
with torch.no_grad():
obs_next_result = self(batch, input='obs_next', explorating=False)
a_ = obs_next_result.act
batch.act = to_torch_as(batch.act, a_)
target_q = torch.min(
self.critic1_old(batch.obs_next, a_),
self.critic2_old(batch.obs_next, a_),
) - self._alpha * obs_next_result.log_prob
return target_q
def learn( # type: ignore
self, batch: Batch, batch_size: int, repeat: int,
**kwargs: Any) -> Dict[str, List[float]]:
losses = []
for _ in range(repeat):
for b in batch.split(batch_size, merge_last=True):
self.optim.zero_grad()
result = self(b)
dist = result.dist
a = to_torch_as(b.act, result.act)
r = to_torch_as(b.returns, result.act)
log_prob = dist.log_prob(a).reshape(len(r), -1).transpose(0, 1)
loss = -(log_prob * r).mean()
loss.backward()
self.optim.step()
losses.append(loss.item())
# update learning rate if lr_scheduler is given
if self.lr_scheduler is not None:
self.lr_scheduler.step()
return {"loss": losses}
def learn(self, batch:Batch, batch_size: int, repeat: int, **kwargs: Any) -> Dict[str, float]:
for _ in range(repeat):
for b in batch.split(batch_size, merge_last=True):
self.optim.zero_grad()
state_values = self.value_net(b.obs, b.act).flatten()
advantages = b.advantages.flatten()
#actions = b.act
dist = self(b).dist
actions = to_torch_as(b.act, dist.logits)
rewards = to_torch_as(b.returns, dist.logits)
# Advantage estimation
adv = advantages - state_values
adv_squared = (adv.pow(2)).mean()
# Value loss
v_loss = 0.5 * adv_squared
# Policy loss
# Update averaged advantage norm
# Exponentially weighted advantages
exp_advs =
# log\pi_\theta(a|s)
log_prob = dist.log_prob(a).reshape(len(rewards), -1).transpose(0, 1)
p_loss = - 1.0 * (log_prob * exp_advs.detach()).mean()
# Combine both losses
loss = p_loss + self.vf_coeff * v_loss
loss.backward()
self.optim.step()
return {
"policy_loss": p_loss.item(),
"vf_loss": v_loss.item(),
"total_loss": loss.item(),
"vf_explained_var": explained_variance.item()
}
def process_fn(self, batch: Batch, buffer: ReplayBuffer,
indice: np.ndarray) -> Batch:
if self._recompute_adv:
# buffer input `buffer` and `indice` to be used in `learn()`.
self._buffer, self._indice = buffer, indice
batch = self._compute_returns(batch, buffer, indice)
batch.act = to_torch_as(batch.act, batch.v_s)
old_log_prob = []
with torch.no_grad():
for b in batch.split(self._batch, shuffle=False, merge_last=True):
old_log_prob.append(self(b).dist.log_prob(b.act))
batch.logp_old = torch.cat(old_log_prob, dim=0)
return batch
def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
if self._target and self._iter % self._freq == 0:
self.sync_weight()
self.optim.zero_grad()
# compute dqn loss
weight = batch.pop("weight", 1.0)
q = self(batch).logits
q = q[np.arange(len(q)), batch.act]
r = to_torch_as(batch.returns.flatten(), q)
weight = to_torch_as(weight, q)
td = r - q
dqn_loss = (td.pow(2) * weight).mean()
batch.weight = td # prio-buffer
# compute opd loss
slow_preds = self(batch, output_dqn=False, output_opd=True).logits
fast_preds = self(batch,
input="fast_obs",
output_dqn=False,
output_opd=True).logits
# act_dim + 1 classes. The last class indicate the state is off-policy
slow_label = torch.ones_like(
slow_preds[:, 0], dtype=torch.int64) * int(self.model.output_dim)
fast_label = to_torch_as(torch.tensor(batch.fast_act), slow_label)
opd_loss = F.cross_entropy(slow_preds, slow_label) + \
F.cross_entropy(fast_preds, fast_label)
# compute loss and back prop
loss = dqn_loss + self.opd_loss_coeff * opd_loss
loss.backward()
self.optim.step()
self._iter += 1
return {
"loss": loss.item(),
"dqn_loss": dqn_loss.item(),
"opd_loss": opd_loss.item(),
}
def learn(self, batch: Batch, **kwargs) -> Dict[str, float]:
if self._target and self._cnt % self._freq == 0:
self.sync_weight()
self.optim.zero_grad()
weight = batch.pop('weight', 1.)
q = self(batch, eps=0., is_learning=True).logits
# q = torch.tensor(q, requires_grad=True)
# q = q[np.arange(len(q)), batch.act]
r = to_torch_as(batch.policy.reshape((-1, 1)), q)
loss = F.mse_loss(r, q)
loss.backward()
self.optim.step()
self._cnt += 1
return {'loss': loss.item()}
def _compute_return(
self,
batch: Batch,
buffer: ReplayBuffer,
indice: np.ndarray,
gamma: float = 0.99,
) -> Batch:
rew = batch.rew
with torch.no_grad():
target_q_torch = self._target_q(buffer, indice) # (bsz, ?)
target_q = to_numpy(target_q_torch)
end_flag = buffer.done.copy()
end_flag[buffer.unfinished_index()] = True
end_flag = end_flag[indice]
mean_target_q = np.mean(target_q, -1) if len(target_q.shape) > 1 else target_q
_target_q = rew + gamma * mean_target_q * (1 - end_flag)
target_q = np.repeat(_target_q[..., None], self.num_branches, axis=-1)
target_q = np.repeat(target_q[..., None], self.max_action_num, axis=-1)
batch.returns = to_torch_as(target_q, target_q_torch)
if hasattr(batch, "weight"): # prio buffer update
batch.weight = to_torch_as(batch.weight, target_q_torch)
return batch
def learn(self, batch: Batch, **kwargs) -> Dict[str, float]:
if self._target and self._cnt % self._freq == 0:
self.sync_weight()
self.optim.zero_grad()
q = self(batch, eps=0.).logits
q = q[np.arange(len(q)), batch.act]
r = to_torch_as(batch.returns, q).flatten()
td = r - q
loss = (td.pow(2) * batch.weight).mean()
batch.weight = td # prio-buffer
loss.backward()
self.optim.step()
self._cnt += 1
return {'loss': loss.item()}
def forward(self, s, a=None):
"""Almost the same as :class:`~tianshou.utils.net.common.Recurrent`."""
s = to_torch(s, device=self.device, dtype=torch.float32)
# s [bsz, len, dim] (training) or [bsz, dim] (evaluation)
# In short, the tensor's shape in training phase is longer than which
# in evaluation phase.
assert len(s.shape) == 3
self.nn.flatten_parameters()
s, (h, c) = self.nn(s)
s = s[:, -1]
if a is not None:
a = to_torch_as(a, s)
s = torch.cat([s, a], dim=1)
s = self.fc2(s)
return s
def forward(
self,
s: Union[np.ndarray, torch.Tensor],
a: Optional[Union[np.ndarray, torch.Tensor]] = None,
info: Dict[str, Any] = {},
) -> torch.Tensor:
"""Mapping: (s, a) -> logits -> Q(s, a)."""
s = to_torch(s, device=self.device, dtype=torch.float32)
s = s.flatten(1)
if a is not None:
a = to_torch_as(a, s)
a = a.flatten(1)
s = torch.cat([s, a], dim=1)
logits, h = self.preprocess(s)
logits = self.last(logits)
return logits
def learn(self, batch: Batch, **kwargs) -> Dict[str, float]:
current_q = self.critic(batch.obs, batch.act)
target_q = to_torch_as(batch.returns, current_q)
target_q = target_q[:, None]
critic_loss = F.mse_loss(current_q, target_q)
self.critic_optim.zero_grad()
critic_loss.backward()
self.critic_optim.step()
actor_loss = -self.critic(batch.obs, self(batch, eps=0).act).mean()
self.actor_optim.zero_grad()
actor_loss.backward()
self.actor_optim.step()
self.sync_weight()
return {
'loss/actor': actor_loss.item(),
'loss/critic': critic_loss.item(),
}