Source code for ding.policy.fqf
from typing import List, Dict, Any, Tuple, Union
import copy
import torch
from ding.torch_utils import Adam, RMSprop, to_device
from ding.rl_utils import fqf_nstep_td_data, fqf_nstep_td_error, fqf_calculate_fraction_loss, \
get_train_sample, 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 .dqn import DQNPolicy
from .common_utils import default_preprocess_learn
[docs]@POLICY_REGISTRY.register('fqf')
class FQFPolicy(DQNPolicy):
r"""
Overview:
Policy class of FQF algorithm.
Config:
== ==================== ======== ============== ======================================== =======================
ID Symbol Type Default Value Description Other(Shape)
== ==================== ======== ============== ======================================== =======================
1 ``type`` str fqf | RL policy register name, refer to | this arg is optional,
| registry ``POLICY_REGISTRY`` | a placeholder
2 ``cuda`` bool False | Whether to use cuda for network | this arg can be diff-
| erent from modes
3 ``on_policy`` bool False | Whether the RL algorithm is on-policy
| or off-policy
4 ``priority`` bool True | Whether use priority(PER) | priority sample,
| update priority
6 | ``other.eps`` float 0.05 | Start value for epsilon decay. It's
| ``.start`` | small because rainbow use noisy net.
7 | ``other.eps`` float 0.05 | End value for epsilon decay.
| ``.end``
8 | ``discount_`` float 0.97, | Reward's future discount factor, aka. | may be 1 when sparse
| ``factor`` [0.95, 0.999] | gamma | reward env
9 ``nstep`` int 3, | N-step reward discount sum for target
[3, 5] | q_value estimation
10 | ``learn.update`` int 3 | How many updates(iterations) to train | this args can be vary
| ``per_collect`` | after collector's one collection. Only | from envs. Bigger val
| valid in serial training | means more off-policy
11 ``learn.kappa`` float / | Threshold of Huber loss
== ==================== ======== ============== ======================================== =======================
"""
config = dict(
# (str) RL policy register name (refer to function "POLICY_REGISTRY").
type='fqf',
# (bool) Whether to use cuda for network.
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=dict(
# How many updates(iterations) to train after collector's one collection.
# Bigger "update_per_collect" means bigger off-policy.
# collect data -> update policy-> collect data -> ...
update_per_collect=3,
batch_size=64,
learning_rate_fraction=2.5e-9,
learning_rate_quantile=0.00005,
# ==============================================================
# The following configs are algorithm-specific
# ==============================================================
# (int) Frequence of target network update.
target_update_freq=100,
# (float) Threshold of Huber loss. In the FQF paper, this is denoted by kappa. Default to 1.0.
kappa=1.0,
# (float) Coefficient of entropy_loss.
ent_coef=0,
# (bool) Whether ignore done(usually for max step termination env)
ignore_done=False,
),
# collect_mode config
collect=dict(
# (int) Only one of [n_sample, n_step, n_episode] shoule be set
# n_sample=8,
# (int) Cut trajectories into pieces with length "unroll_len".
unroll_len=1,
),
eval=dict(),
# other config
other=dict(
# Epsilon greedy with decay.
eps=dict(
# (str) Decay type. Support ['exp', 'linear'].
type='exp',
start=0.95,
end=0.1,
# (int) Decay length(env step)
decay=10000,
),
replay_buffer=dict(replay_buffer_size=10000, )
),
)
def default_model(self) -> Tuple[str, List[str]]:
return 'fqf', ['ding.model.template.q_learning']
def _init_learn(self) -> None:
r"""
Overview:
Learn mode init method. Called by ``self.__init__``.
Init the optimizer, algorithm config, main and target models.
"""
self._priority = self._cfg.priority
# Optimizer
self._fraction_loss_optimizer = RMSprop(
self._model.head.quantiles_proposal.parameters(),
lr=self._cfg.learn.learning_rate_fraction,
alpha=0.95,
eps=0.00001
)
self._quantile_loss_optimizer = Adam(
list(self._model.head.Q.parameters()) + list(self._model.head.fqf_fc.parameters()) +
list(self._model.encoder.parameters()),
lr=self._cfg.learn.learning_rate_quantile,
eps=1e-2 / self._cfg.learn.batch_size
)
self._gamma = self._cfg.discount_factor
self._nstep = self._cfg.nstep
self._kappa = self._cfg.learn.kappa
self._ent_coef = self._cfg.learn.ent_coef
# use model_wrapper for specialized demands of different modes
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()
def _forward_learn(self, data: dict) -> Dict[str, Any]:
r"""
Overview:
Forward and backward function of learn mode.
Arguments:
- data (:obj:`dict`): Dict type data, including at least ['obs', 'action', 'reward', 'next_obs']
Returns:
- info_dict (:obj:`Dict[str, Any]`): Including current lr and loss.
"""
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)
# ====================
# Q-learning forward
# ====================
self._learn_model.train()
self._target_model.train()
# Current q value (main model)
ret = self._learn_model.forward(data['obs'])
logit = ret['logit'] # [batch, action_dim(64)]
q_value = ret['q'] # [batch, num_quantiles, action_dim(64)]
quantiles = ret['quantiles'] # [batch, num_quantiles+1]
quantiles_hats = ret['quantiles_hats'] # [batch, num_quantiles], requires_grad = False
q_tau_i = ret['q_tau_i'] # [batch_size, num_quantiles-1, action_dim(64)]
entropies = ret['entropies'] # [batch, 1]
# 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']
data_n = fqf_nstep_td_data(
q_value, target_q_value, data['action'], target_q_action, data['reward'], data['done'], quantiles_hats,
data['weight']
)
value_gamma = data.get('value_gamma')
entropy_loss = -self._ent_coef * entropies.mean()
fraction_loss = fqf_calculate_fraction_loss(q_tau_i.detach(), q_value, quantiles, data['action']) + entropy_loss
quantile_loss, td_error_per_sample = fqf_nstep_td_error(
data_n, self._gamma, nstep=self._nstep, kappa=self._kappa, value_gamma=value_gamma
)
# compute grad norm of a network's parameters
def compute_grad_norm(model):
return torch.norm(torch.stack([torch.norm(p.grad.detach(), 2.0) for p in model.parameters()]), 2.0)
# ====================
# fraction_proposal network update
# ====================
self._fraction_loss_optimizer.zero_grad()
fraction_loss.backward(retain_graph=True)
if self._cfg.multi_gpu:
self.sync_gradients(self._learn_model)
with torch.no_grad():
total_norm_quantiles_proposal = compute_grad_norm(self._model.head.quantiles_proposal)
self._fraction_loss_optimizer.step()
# ====================
# Q-learning update
# ====================
self._quantile_loss_optimizer.zero_grad()
quantile_loss.backward()
if self._cfg.multi_gpu:
self.sync_gradients(self._learn_model)
with torch.no_grad():
total_norm_Q = compute_grad_norm(self._model.head.Q)
total_norm_fqf_fc = compute_grad_norm(self._model.head.fqf_fc)
total_norm_encoder = compute_grad_norm(self._model.encoder)
self._quantile_loss_optimizer.step()
# =============
# after update
# =============
self._target_model.update(self._learn_model.state_dict())
return {
'cur_lr_fraction_loss': self._fraction_loss_optimizer.defaults['lr'],
'cur_lr_quantile_loss': self._quantile_loss_optimizer.defaults['lr'],
'logit': logit.mean().item(),
'fraction_loss': fraction_loss.item(),
'quantile_loss': quantile_loss.item(),
'total_norm_quantiles_proposal': total_norm_quantiles_proposal,
'total_norm_Q': total_norm_Q,
'total_norm_fqf_fc': total_norm_fqf_fc,
'total_norm_encoder': total_norm_encoder,
'priority': td_error_per_sample.abs().tolist(),
# Only discrete action satisfying len(data['action'])==1 can return this and draw histogram on tensorboard.
'[histogram]action_distribution': data['action'],
'[histogram]quantiles_hats': quantiles_hats[0], # quantiles_hats.requires_grad = False
}
def _monitor_vars_learn(self) -> List[str]:
return [
'cur_lr_fraction_loss', 'cur_lr_quantile_loss', 'logit', 'fraction_loss', 'quantile_loss',
'total_norm_quantiles_proposal', 'total_norm_Q', 'total_norm_fqf_fc', 'total_norm_encoder'
]
def _state_dict_learn(self) -> Dict[str, Any]:
return {
'model': self._learn_model.state_dict(),
'target_model': self._target_model.state_dict(),
'optimizer_fraction_loss': self._fraction_loss_optimizer.state_dict(),
'optimizer_quantile_loss': self._quantile_loss_optimizer.state_dict(),
}
def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None:
self._learn_model.load_state_dict(state_dict['model'])
self._target_model.load_state_dict(state_dict['target_model'])
self._fraction_loss_optimizer.load_state_dict(state_dict['optimizer_fraction_loss'])
self._quantile_loss_optimizer.load_state_dict(state_dict['optimizer_quantile_loss'])