Source code for ding.policy.cql
from typing import List, Dict, Any, Tuple, Union
import copy
import numpy as np
import torch
import torch.nn.functional as F
from torch.distributions import Normal, Independent
from ding.torch_utils import Adam, to_device
from ding.rl_utils import v_1step_td_data, v_1step_td_error, get_train_sample, \
qrdqn_nstep_td_data, qrdqn_nstep_td_error, get_nstep_return_data
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY
from ding.utils.data import default_collate, default_decollate
from .sac import SACPolicy
from .qrdqn import QRDQNPolicy
from .common_utils import default_preprocess_learn
[docs]@POLICY_REGISTRY.register('cql')
class CQLPolicy(SACPolicy):
"""
Overview:
Policy class of CQL algorithm for continuous control. Paper link: https://arxiv.org/abs/2006.04779.
Config:
== ==================== ======== ============= ================================= =======================
ID Symbol Type Default Value Description Other(Shape)
== ==================== ======== ============= ================================= =======================
1 ``type`` str cql | RL policy register name, refer | this arg is optional,
| to registry ``POLICY_REGISTRY`` | a placeholder
2 ``cuda`` bool True | Whether to use cuda for network |
3 | ``random_`` int 10000 | Number of randomly collected | Default to 10000 for
| ``collect_size`` | training samples in replay | SAC, 25000 for DDPG/
| | buffer when training starts. | TD3.
4 | ``model.policy_`` int 256 | Linear layer size for policy |
| ``embedding_size`` | network. |
5 | ``model.soft_q_`` int 256 | Linear layer size for soft q |
| ``embedding_size`` | network. |
6 | ``model.value_`` int 256 | Linear layer size for value | Defalut to None when
| ``embedding_size`` | network. | model.value_network
| | | is False.
7 | ``learn.learning`` float 3e-4 | Learning rate for soft q | Defalut to 1e-3, when
| ``_rate_q`` | network. | model.value_network
| | | is True.
8 | ``learn.learning`` float 3e-4 | Learning rate for policy | Defalut to 1e-3, when
| ``_rate_policy`` | network. | model.value_network
| | | is True.
9 | ``learn.learning`` float 3e-4 | Learning rate for policy | Defalut to None when
| ``_rate_value`` | network. | model.value_network
| | | is False.
10 | ``learn.alpha`` float 0.2 | Entropy regularization | alpha is initiali-
| | coefficient. | zation for auto
| | | `alpha`, when
| | | auto_alpha is True
11 | ``learn.repara_`` bool True | Determine whether to use |
| ``meterization`` | reparameterization trick. |
12 | ``learn.`` bool False | Determine whether to use | Temperature parameter
| ``auto_alpha`` | auto temperature parameter | determines the
| | `alpha`. | relative importance
| | | of the entropy term
| | | against the reward.
13 | ``learn.-`` bool False | Determine whether to ignore | Use ignore_done only
| ``ignore_done`` | done flag. | in halfcheetah env.
14 | ``learn.-`` float 0.005 | Used for soft update of the | aka. Interpolation
| ``target_theta`` | target network. | factor in polyak aver
| | | aging for target
| | | networks.
== ==================== ======== ============= ================================= =======================
"""
config = dict(
# (str) RL policy register name (refer to function "POLICY_REGISTRY").
type='cql',
# (bool) Whether to use cuda for policy.
cuda=False,
# (bool) on_policy: Determine whether on-policy or off-policy.
# on-policy setting influences the behaviour of buffer.
on_policy=False,
# (bool) priority: Determine whether to use priority in buffer sample.
priority=False,
# (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.
priority_IS_weight=False,
# (int) Number of training samples(randomly collected) in replay buffer when training starts.
random_collect_size=10000,
model=dict(
# (bool type) twin_critic: Determine whether to use double-soft-q-net for target q computation.
# Please refer to TD3 about Clipped Double-Q Learning trick, which learns two Q-functions instead of one .
# Default to True.
twin_critic=True,
# (str type) action_space: Use reparameterization trick for continous action
action_space='reparameterization',
# (int) Hidden size for actor network head.
actor_head_hidden_size=256,
# (int) Hidden size for critic network head.
critic_head_hidden_size=256,
),
# learn_mode config
learn=dict(
# (int) How many updates (iterations) to train after collector's one collection.
# Bigger "update_per_collect" means bigger off-policy.
update_per_collect=1,
# (int) Minibatch size for gradient descent.
batch_size=256,
# (float) learning_rate_q: Learning rate for soft q network.
learning_rate_q=3e-4,
# (float) learning_rate_policy: Learning rate for policy network.
learning_rate_policy=3e-4,
# (float) learning_rate_alpha: Learning rate for auto temperature parameter ``alpha``.
learning_rate_alpha=3e-4,
# (float) target_theta: Used for soft update of the target network,
# aka. Interpolation factor in polyak averaging for target networks.
target_theta=0.005,
# (float) discount factor for the discounted sum of rewards, aka. gamma.
discount_factor=0.99,
# (float) alpha: Entropy regularization coefficient.
# Please check out the original SAC paper (arXiv 1801.01290): Eq 1 for more details.
# If auto_alpha is set to `True`, alpha is initialization for auto `\alpha`.
# Default to 0.2.
alpha=0.2,
# (bool) auto_alpha: Determine whether to use auto temperature parameter `\alpha` .
# Temperature parameter determines the relative importance of the entropy term against the reward.
# Please check out the original SAC paper (arXiv 1801.01290): Eq 1 for more details.
# Default to False.
# Note that: Using auto alpha needs to set learning_rate_alpha in `cfg.policy.learn`.
auto_alpha=True,
# (bool) log_space: Determine whether to use auto `\alpha` in log space.
log_space=True,
# (bool) Whether ignore done(usually for max step termination env. e.g. pendulum)
# Note: Gym wraps the MuJoCo envs by default with TimeLimit environment wrappers.
# These limit HalfCheetah, and several other MuJoCo envs, to max length of 1000.
# However, interaction with HalfCheetah always gets done with done is False,
# Since we inplace done==True with done==False to keep
# TD-error accurate computation(``gamma * (1 - done) * next_v + reward``),
# when the episode step is greater than max episode step.
ignore_done=False,
# (float) Weight uniform initialization range in the last output layer.
init_w=3e-3,
# (int) The numbers of action sample each at every state s from a uniform-at-random.
num_actions=10,
# (bool) Whether use lagrange multiplier in q value loss.
with_lagrange=False,
# (float) The threshold for difference in Q-values.
lagrange_thresh=-1,
# (float) Loss weight for conservative item.
min_q_weight=1.0,
# (bool) Whether to use entropy in target q.
with_q_entropy=False,
),
eval=dict(), # for compatibility
)
[docs] def _init_learn(self) -> None:
"""
Overview:
Initialize the learn mode of policy, including related attributes and modules. For SAC, it mainly \
contains three optimizers, algorithm-specific arguments such as gamma, min_q_weight, with_lagrange and \
with_q_entropy, main and target model. Especially, the ``auto_alpha`` mechanism for balancing max entropy \
target is also initialized here.
This method will be called in ``__init__`` method if ``learn`` field is in ``enable_field``.
.. note::
For the member variables that need to be saved and loaded, please refer to the ``_state_dict_learn`` \
and ``_load_state_dict_learn`` methods.
.. note::
For the member variables that need to be monitored, please refer to the ``_monitor_vars_learn`` method.
.. note::
If you want to set some spacial member variables in ``_init_learn`` method, you'd better name them \
with prefix ``_learn_`` to avoid conflict with other modes, such as ``self._learn_attr1``.
"""
self._priority = self._cfg.priority
self._priority_IS_weight = self._cfg.priority_IS_weight
self._twin_critic = self._cfg.model.twin_critic
self._num_actions = self._cfg.learn.num_actions
self._min_q_version = 3
self._min_q_weight = self._cfg.learn.min_q_weight
self._with_lagrange = self._cfg.learn.with_lagrange and (self._lagrange_thresh > 0)
self._lagrange_thresh = self._cfg.learn.lagrange_thresh
if self._with_lagrange:
self.target_action_gap = self._lagrange_thresh
self.log_alpha_prime = torch.tensor(0.).to(self._device).requires_grad_()
self.alpha_prime_optimizer = Adam(
[self.log_alpha_prime],
lr=self._cfg.learn.learning_rate_q,
)
self._with_q_entropy = self._cfg.learn.with_q_entropy
# Weight Init
init_w = self._cfg.learn.init_w
self._model.actor_head[-1].mu.weight.data.uniform_(-init_w, init_w)
self._model.actor_head[-1].mu.bias.data.uniform_(-init_w, init_w)
self._model.actor_head[-1].log_sigma_layer.weight.data.uniform_(-init_w, init_w)
self._model.actor_head[-1].log_sigma_layer.bias.data.uniform_(-init_w, init_w)
if self._twin_critic:
self._model.critic_head[0][-1].last.weight.data.uniform_(-init_w, init_w)
self._model.critic_head[0][-1].last.bias.data.uniform_(-init_w, init_w)
self._model.critic_head[1][-1].last.weight.data.uniform_(-init_w, init_w)
self._model.critic_head[1][-1].last.bias.data.uniform_(-init_w, init_w)
else:
self._model.critic_head[2].last.weight.data.uniform_(-init_w, init_w)
self._model.critic_head[-1].last.bias.data.uniform_(-init_w, init_w)
# Optimizers
self._optimizer_q = Adam(
self._model.critic.parameters(),
lr=self._cfg.learn.learning_rate_q,
)
self._optimizer_policy = Adam(
self._model.actor.parameters(),
lr=self._cfg.learn.learning_rate_policy,
)
# Algorithm config
self._gamma = self._cfg.learn.discount_factor
# Init auto alpha
if self._cfg.learn.auto_alpha:
if self._cfg.learn.target_entropy is None:
assert 'action_shape' in self._cfg.model, "CQL need network model with action_shape variable"
self._target_entropy = -np.prod(self._cfg.model.action_shape)
else:
self._target_entropy = self._cfg.learn.target_entropy
if self._cfg.learn.log_space:
self._log_alpha = torch.log(torch.FloatTensor([self._cfg.learn.alpha]))
self._log_alpha = self._log_alpha.to(self._device).requires_grad_()
self._alpha_optim = torch.optim.Adam([self._log_alpha], lr=self._cfg.learn.learning_rate_alpha)
assert self._log_alpha.shape == torch.Size([1]) and self._log_alpha.requires_grad
self._alpha = self._log_alpha.detach().exp()
self._auto_alpha = True
self._log_space = True
else:
self._alpha = torch.FloatTensor([self._cfg.learn.alpha]).to(self._device).requires_grad_()
self._alpha_optim = torch.optim.Adam([self._alpha], lr=self._cfg.learn.learning_rate_alpha)
self._auto_alpha = True
self._log_space = False
else:
self._alpha = torch.tensor(
[self._cfg.learn.alpha], requires_grad=False, device=self._device, dtype=torch.float32
)
self._auto_alpha = False
# Main and target models
self._target_model = copy.deepcopy(self._model)
self._target_model = model_wrap(
self._target_model,
wrapper_name='target',
update_type='momentum',
update_kwargs={'theta': self._cfg.learn.target_theta}
)
self._learn_model = model_wrap(self._model, wrapper_name='base')
self._learn_model.reset()
self._target_model.reset()
self._forward_learn_cnt = 0
[docs] def _forward_learn(self, data: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
Overview:
Policy forward function of learn mode (training policy and updating parameters). Forward means \
that the policy inputs some training batch data from the offline dataset and then returns the output \
result, including various training information such as loss, action, priority.
Arguments:
- data (:obj:`List[Dict[int, Any]]`): The input data used for policy forward, including a batch of \
training samples. For each element in list, the key of the dict is the name of data items and the \
value is the corresponding data. Usually, the value is torch.Tensor or np.ndarray or there dict/list \
combinations. In the ``_forward_learn`` method, data often need to first be stacked in the batch \
dimension by some utility functions such as ``default_preprocess_learn``. \
For CQL, each element in list is a dict containing at least the following keys: ``obs``, ``action``, \
``reward``, ``next_obs``, ``done``. Sometimes, it also contains other keys such as ``weight``.
Returns:
- info_dict (:obj:`Dict[str, Any]`): The information dict that indicated training result, which will be \
recorded in text log and tensorboard, values must be python scalar or a list of scalars. For the \
detailed definition of the dict, refer to the code of ``_monitor_vars_learn`` method.
.. note::
The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \
For the data type that not supported, the main reason is that the corresponding model does not support it. \
You can implement you own model rather than use the default model. For more information, please raise an \
issue in GitHub repo and we will continue to follow up.
"""
loss_dict = {}
data = default_preprocess_learn(
data,
use_priority=self._priority,
use_priority_IS_weight=self._cfg.priority_IS_weight,
ignore_done=self._cfg.learn.ignore_done,
use_nstep=False
)
if len(data.get('action').shape) == 1:
data['action'] = data['action'].reshape(-1, 1)
if self._cuda:
data = to_device(data, self._device)
self._learn_model.train()
self._target_model.train()
obs = data['obs']
next_obs = data['next_obs']
reward = data['reward']
done = data['done']
# 1. predict q value
q_value = self._learn_model.forward(data, mode='compute_critic')['q_value']
# 2. predict target value
with torch.no_grad():
(mu, sigma) = self._learn_model.forward(next_obs, mode='compute_actor')['logit']
dist = Independent(Normal(mu, sigma), 1)
pred = dist.rsample()
next_action = torch.tanh(pred)
y = 1 - next_action.pow(2) + 1e-6
next_log_prob = dist.log_prob(pred).unsqueeze(-1)
next_log_prob = next_log_prob - torch.log(y).sum(-1, keepdim=True)
next_data = {'obs': next_obs, 'action': next_action}
target_q_value = self._target_model.forward(next_data, mode='compute_critic')['q_value']
# the value of a policy according to the maximum entropy objective
if self._twin_critic:
# find min one as target q value
if self._with_q_entropy:
target_q_value = torch.min(target_q_value[0],
target_q_value[1]) - self._alpha * next_log_prob.squeeze(-1)
else:
target_q_value = torch.min(target_q_value[0], target_q_value[1])
else:
if self._with_q_entropy:
target_q_value = target_q_value - self._alpha * next_log_prob.squeeze(-1)
# 3. compute q loss
if self._twin_critic:
q_data0 = v_1step_td_data(q_value[0], target_q_value, reward, done, data['weight'])
loss_dict['critic_loss'], td_error_per_sample0 = v_1step_td_error(q_data0, self._gamma)
q_data1 = v_1step_td_data(q_value[1], target_q_value, reward, done, data['weight'])
loss_dict['twin_critic_loss'], td_error_per_sample1 = v_1step_td_error(q_data1, self._gamma)
td_error_per_sample = (td_error_per_sample0 + td_error_per_sample1) / 2
else:
q_data = v_1step_td_data(q_value, target_q_value, reward, done, data['weight'])
loss_dict['critic_loss'], td_error_per_sample = v_1step_td_error(q_data, self._gamma)
# 4. add CQL
curr_actions_tensor, curr_log_pis = self._get_policy_actions(data, self._num_actions)
new_curr_actions_tensor, new_log_pis = self._get_policy_actions({'obs': next_obs}, self._num_actions)
random_actions_tensor = torch.FloatTensor(curr_actions_tensor.shape).uniform_(-1,
1).to(curr_actions_tensor.device)
obs_repeat = obs.unsqueeze(1).repeat(1, self._num_actions,
1).view(obs.shape[0] * self._num_actions, obs.shape[1])
act_repeat = data['action'].unsqueeze(1).repeat(1, self._num_actions, 1).view(
data['action'].shape[0] * self._num_actions, data['action'].shape[1]
)
q_rand = self._get_q_value({'obs': obs_repeat, 'action': random_actions_tensor})
# q2_rand = self._get_q_value(obs, random_actions_tensor, network=self.qf2)
q_curr_actions = self._get_q_value({'obs': obs_repeat, 'action': curr_actions_tensor})
# q2_curr_actions = self._get_tensor_values(obs, curr_actions_tensor, network=self.qf2)
q_next_actions = self._get_q_value({'obs': obs_repeat, 'action': new_curr_actions_tensor})
# q2_next_actions = self._get_tensor_values(obs, new_curr_actions_tensor, network=self.qf2)
cat_q1 = torch.cat([q_rand[0], q_value[0].reshape(-1, 1, 1), q_next_actions[0], q_curr_actions[0]], 1)
cat_q2 = torch.cat([q_rand[1], q_value[1].reshape(-1, 1, 1), q_next_actions[1], q_curr_actions[1]], 1)
std_q1 = torch.std(cat_q1, dim=1)
std_q2 = torch.std(cat_q2, dim=1)
if self._min_q_version == 3:
# importance sampled version
random_density = np.log(0.5 ** curr_actions_tensor.shape[-1])
cat_q1 = torch.cat(
[
q_rand[0] - random_density, q_next_actions[0] - new_log_pis.detach(),
q_curr_actions[0] - curr_log_pis.detach()
], 1
)
cat_q2 = torch.cat(
[
q_rand[1] - random_density, q_next_actions[1] - new_log_pis.detach(),
q_curr_actions[1] - curr_log_pis.detach()
], 1
)
min_qf1_loss = torch.logsumexp(cat_q1, dim=1).mean() * self._min_q_weight
min_qf2_loss = torch.logsumexp(cat_q2, dim=1).mean() * self._min_q_weight
"""Subtract the log likelihood of data"""
min_qf1_loss = min_qf1_loss - q_value[0].mean() * self._min_q_weight
min_qf2_loss = min_qf2_loss - q_value[1].mean() * self._min_q_weight
if self._with_lagrange:
alpha_prime = torch.clamp(self.log_alpha_prime.exp(), min=0.0, max=1000000.0)
min_qf1_loss = alpha_prime * (min_qf1_loss - self.target_action_gap)
min_qf2_loss = alpha_prime * (min_qf2_loss - self.target_action_gap)
self.alpha_prime_optimizer.zero_grad()
alpha_prime_loss = (-min_qf1_loss - min_qf2_loss) * 0.5
alpha_prime_loss.backward(retain_graph=True)
self.alpha_prime_optimizer.step()
loss_dict['critic_loss'] += min_qf1_loss
if self._twin_critic:
loss_dict['twin_critic_loss'] += min_qf2_loss
# 5. update q network
self._optimizer_q.zero_grad()
loss_dict['critic_loss'].backward(retain_graph=True)
if self._twin_critic:
loss_dict['twin_critic_loss'].backward()
self._optimizer_q.step()
# 6. evaluate to get action distribution
(mu, sigma) = self._learn_model.forward(data['obs'], mode='compute_actor')['logit']
dist = Independent(Normal(mu, sigma), 1)
pred = dist.rsample()
action = torch.tanh(pred)
y = 1 - action.pow(2) + 1e-6
log_prob = dist.log_prob(pred).unsqueeze(-1)
log_prob = log_prob - torch.log(y).sum(-1, keepdim=True)
eval_data = {'obs': obs, 'action': action}
new_q_value = self._learn_model.forward(eval_data, mode='compute_critic')['q_value']
if self._twin_critic:
new_q_value = torch.min(new_q_value[0], new_q_value[1])
# 8. compute policy loss
policy_loss = (self._alpha * log_prob - new_q_value.unsqueeze(-1)).mean()
loss_dict['policy_loss'] = policy_loss
# 9. update policy network
self._optimizer_policy.zero_grad()
loss_dict['policy_loss'].backward()
self._optimizer_policy.step()
# 10. compute alpha loss
if self._auto_alpha:
if self._log_space:
log_prob = log_prob + self._target_entropy
loss_dict['alpha_loss'] = -(self._log_alpha * log_prob.detach()).mean()
self._alpha_optim.zero_grad()
loss_dict['alpha_loss'].backward()
self._alpha_optim.step()
self._alpha = self._log_alpha.detach().exp()
else:
log_prob = log_prob + self._target_entropy
loss_dict['alpha_loss'] = -(self._alpha * log_prob.detach()).mean()
self._alpha_optim.zero_grad()
loss_dict['alpha_loss'].backward()
self._alpha_optim.step()
self._alpha = max(0, self._alpha)
loss_dict['total_loss'] = sum(loss_dict.values())
# =============
# after update
# =============
self._forward_learn_cnt += 1
# target update
self._target_model.update(self._learn_model.state_dict())
return {
'cur_lr_q': self._optimizer_q.defaults['lr'],
'cur_lr_p': self._optimizer_policy.defaults['lr'],
'priority': td_error_per_sample.abs().tolist(),
'td_error': td_error_per_sample.detach().mean().item(),
'alpha': self._alpha.item(),
'target_q_value': target_q_value.detach().mean().item(),
**loss_dict
}
def _get_policy_actions(self, data: Dict, num_actions: int = 10, epsilon: float = 1e-6) -> List:
# evaluate to get action distribution
obs = data['obs']
obs = obs.unsqueeze(1).repeat(1, num_actions, 1).view(obs.shape[0] * num_actions, obs.shape[1])
(mu, sigma) = self._learn_model.forward(obs, mode='compute_actor')['logit']
dist = Independent(Normal(mu, sigma), 1)
pred = dist.rsample()
action = torch.tanh(pred)
# evaluate action log prob depending on Jacobi determinant.
y = 1 - action.pow(2) + epsilon
log_prob = dist.log_prob(pred).unsqueeze(-1)
log_prob = log_prob - torch.log(y).sum(-1, keepdim=True)
return action, log_prob.view(-1, num_actions, 1)
def _get_q_value(self, data: Dict, keep: bool = True) -> torch.Tensor:
new_q_value = self._learn_model.forward(data, mode='compute_critic')['q_value']
if self._twin_critic:
new_q_value = [value.view(-1, self._num_actions, 1) for value in new_q_value]
else:
new_q_value = new_q_value.view(-1, self._num_actions, 1)
if self._twin_critic and not keep:
new_q_value = torch.min(new_q_value[0], new_q_value[1])
return new_q_value
[docs]@POLICY_REGISTRY.register('discrete_cql')
class DiscreteCQLPolicy(QRDQNPolicy):
"""
Overview:
Policy class of discrete CQL algorithm in discrete action space environments.
Paper link: https://arxiv.org/abs/2006.04779.
"""
config = dict(
# (str) RL policy register name (refer to function "POLICY_REGISTRY").
type='discrete_cql',
# (bool) Whether to use cuda for policy.
cuda=False,
# (bool) Whether the RL algorithm is on-policy or off-policy.
on_policy=False,
# (bool) Whether use priority(priority sample, IS weight, update priority)
priority=False,
# (float) Reward's future discount factor, aka. gamma.
discount_factor=0.97,
# (int) N-step reward for target q_value estimation
nstep=1,
# learn_mode config
learn=dict(
# (int) How many updates (iterations) to train after collector's one collection.
# Bigger "update_per_collect" means bigger off-policy.
update_per_collect=1,
# (int) Minibatch size for one gradient descent.
batch_size=64,
# (float) Learning rate for soft q network.
learning_rate=0.001,
# (int) Frequence of target network update.
target_update_freq=100,
# (bool) Whether ignore done(usually for max step termination env).
ignore_done=False,
# (float) Loss weight for conservative item.
min_q_weight=1.0,
),
eval=dict(), # for compatibility
)
[docs] def _init_learn(self) -> None:
"""
Overview:
Initialize the learn mode of policy, including related attributes and modules. For DiscreteCQL, it mainly \
contains the optimizer, algorithm-specific arguments such as gamma, nstep and min_q_weight, main and \
target model. This method will be called in ``__init__`` method if ``learn`` field is in ``enable_field``.
.. note::
For the member variables that need to be saved and loaded, please refer to the ``_state_dict_learn`` \
and ``_load_state_dict_learn`` methods.
.. note::
For the member variables that need to be monitored, please refer to the ``_monitor_vars_learn`` method.
.. note::
If you want to set some spacial member variables in ``_init_learn`` method, you'd better name them \
with prefix ``_learn_`` to avoid conflict with other modes, such as ``self._learn_attr1``.
"""
self._min_q_weight = self._cfg.learn.min_q_weight
self._priority = self._cfg.priority
# Optimizer
self._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate)
self._gamma = self._cfg.discount_factor
self._nstep = self._cfg.nstep
# use wrapper instead of plugin
self._target_model = copy.deepcopy(self._model)
self._target_model = model_wrap(
self._target_model,
wrapper_name='target',
update_type='assign',
update_kwargs={'freq': self._cfg.learn.target_update_freq}
)
self._learn_model = model_wrap(self._model, wrapper_name='argmax_sample')
self._learn_model.reset()
self._target_model.reset()
[docs] def _forward_learn(self, data: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
Overview:
Policy forward function of learn mode (training policy and updating parameters). Forward means \
that the policy inputs some training batch data from the offline dataset and then returns the output \
result, including various training information such as loss, action, priority.
Arguments:
- data (:obj:`List[Dict[int, Any]]`): The input data used for policy forward, including a batch of \
training samples. For each element in list, the key of the dict is the name of data items and the \
value is the corresponding data. Usually, the value is torch.Tensor or np.ndarray or there dict/list \
combinations. In the ``_forward_learn`` method, data often need to first be stacked in the batch \
dimension by some utility functions such as ``default_preprocess_learn``. \
For DiscreteCQL, each element in list is a dict containing at least the following keys: ``obs``, \
``action``, ``reward``, ``next_obs``, ``done``. Sometimes, it also contains other keys like ``weight`` \
and ``value_gamma`` for nstep return computation.
Returns:
- info_dict (:obj:`Dict[str, Any]`): The information dict that indicated training result, which will be \
recorded in text log and tensorboard, values must be python scalar or a list of scalars. For the \
detailed definition of the dict, refer to the code of ``_monitor_vars_learn`` method.
.. note::
The input value can be torch.Tensor or dict/list combinations and current policy supports all of them. \
For the data type that not supported, the main reason is that the corresponding model does not support it. \
You can implement you own model rather than use the default model. For more information, please raise an \
issue in GitHub repo and we will continue to follow up.
"""
data = default_preprocess_learn(
data, use_priority=self._priority, ignore_done=self._cfg.learn.ignore_done, use_nstep=True
)
if self._cuda:
data = to_device(data, self._device)
if data['action'].dim() == 2 and data['action'].shape[-1] == 1:
data['action'] = data['action'].squeeze(-1)
# ====================
# Q-learning forward
# ====================
self._learn_model.train()
self._target_model.train()
# Current q value (main model)
ret = self._learn_model.forward(data['obs'])
q_value, tau = ret['q'], ret['tau']
# Target q value
with torch.no_grad():
target_q_value = self._target_model.forward(data['next_obs'])['q']
# Max q value action (main model)
target_q_action = self._learn_model.forward(data['next_obs'])['action']
# add CQL
# 1. chose action and compute q in dataset.
# 2. compute value loss(negative_sampling - dataset_expec)
replay_action_one_hot = F.one_hot(data['action'], self._cfg.model.action_shape)
replay_chosen_q = (q_value.mean(-1) * replay_action_one_hot).sum(dim=1)
dataset_expec = replay_chosen_q.mean()
negative_sampling = torch.logsumexp(q_value.mean(-1), dim=1).mean()
min_q_loss = negative_sampling - dataset_expec
data_n = qrdqn_nstep_td_data(
q_value, target_q_value, data['action'], target_q_action, data['reward'], data['done'], tau, data['weight']
)
value_gamma = data.get('value_gamma')
loss, td_error_per_sample = qrdqn_nstep_td_error(
data_n, self._gamma, nstep=self._nstep, value_gamma=value_gamma
)
loss += self._min_q_weight * min_q_loss
# ====================
# Q-learning update
# ====================
self._optimizer.zero_grad()
loss.backward()
if self._cfg.multi_gpu:
self.sync_gradients(self._learn_model)
self._optimizer.step()
# =============
# after update
# =============
self._target_model.update(self._learn_model.state_dict())
return {
'cur_lr': self._optimizer.defaults['lr'],
'total_loss': loss.item(),
'priority': td_error_per_sample.abs().tolist(),
'q_target': target_q_value.mean().item(),
'q_value': q_value.mean().item(),
# Only discrete action satisfying len(data['action'])==1 can return this and draw histogram on tensorboard.
# '[histogram]action_distribution': data['action'],
}
[docs] def _monitor_vars_learn(self) -> List[str]:
"""
Overview:
Return the necessary keys for logging the return dict of ``self._forward_learn``. The logger module, such \
as text logger, tensorboard logger, will use these keys to save the corresponding data.
Returns:
- necessary_keys (:obj:`List[str]`): The list of the necessary keys to be logged.
"""
return ['cur_lr', 'total_loss', 'q_target', 'q_value']