Source code for ding.policy.mdqn
from typing import List, Dict, Any
import copy
import torch
from ding.torch_utils import Adam, to_device
from ding.rl_utils import m_q_1step_td_data, m_q_1step_td_error
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY
from .dqn import DQNPolicy
from .common_utils import default_preprocess_learn
[docs]@POLICY_REGISTRY.register('mdqn')
class MDQNPolicy(DQNPolicy):
"""
Overview:
Policy class of Munchausen DQN algorithm, extended by auxiliary objectives.
Paper link: https://arxiv.org/abs/2007.14430.
Config:
== ==================== ======== ============== ======================================== =======================
ID Symbol Type Default Value Description Other(Shape)
== ==================== ======== ============== ======================================== =======================
1 ``type`` str mdqn | 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 False | Whether use priority(PER) | Priority sample,
| update priority
5 | ``priority_IS`` bool False | Whether use Importance Sampling Weight
| ``_weight`` | to correct biased update. If True,
| priority must be True.
6 | ``discount_`` float 0.97, | Reward's future discount factor, aka. | May be 1 when sparse
| ``factor`` [0.95, 0.999] | gamma | reward env
7 ``nstep`` int 1, | N-step reward discount sum for target
[3, 5] | q_value estimation
8 | ``learn.update`` int 1 | 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
| ``_gpu``
10 | ``learn.batch_`` int 32 | The number of samples of an iteration
| ``size``
11 | ``learn.learning`` float 0.001 | Gradient step length of an iteration.
| ``_rate``
12 | ``learn.target_`` int 2000 | Frequence of target network update. | Hard(assign) update
| ``update_freq``
13 | ``learn.ignore_`` bool False | Whether ignore done for target value | Enable it for some
| ``done`` | calculation. | fake termination env
14 ``collect.n_sample`` int 4 | The number of training samples of a | It varies from
| call of collector. | different envs
15 | ``collect.unroll`` int 1 | unroll length of an iteration | In RNN, unroll_len>1
| ``_len``
16 | ``other.eps.type`` str exp | exploration rate decay type | Support ['exp',
| 'linear'].
17 | ``other.eps.`` float 0.01 | start value of exploration rate | [0,1]
| ``start``
18 | ``other.eps.`` float 0.001 | end value of exploration rate | [0,1]
| ``end``
19 | ``other.eps.`` int 250000 | decay length of exploration | greater than 0. set
| ``decay`` | decay=250000 means
| the exploration rate
| decay from start
| value to end value
| during decay length.
20 | ``entropy_tau`` float 0.003 | the ration of entropy in TD loss
21 | ``alpha`` float 0.9 | the ration of Munchausen term to the
| TD loss
== ==================== ======== ============== ======================================== =======================
"""
config = dict(
# (str) RL policy register name (refer to function "POLICY_REGISTRY").
type='mdqn',
# (bool) Whether to use cuda in policy.
cuda=False,
# (bool) Whether learning policy is the same as collecting data policy(on-policy).
on_policy=False,
# (bool) Whether to enable priority experience sample.
priority=False,
# (bool) Whether to use Importance Sampling Weight to correct biased update. If True, priority must be True.
priority_IS_weight=False,
# (float) Discount factor(gamma) for returns.
discount_factor=0.97,
# (float) Entropy factor (tau) for Munchausen DQN.
entropy_tau=0.03,
# (float) Discount factor (alpha) for Munchausen term.
m_alpha=0.9,
# (int) The number of step for calculating target q_value.
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.
# collect data -> update policy-> collect data -> ...
update_per_collect=3,
# (int) How many samples in a training batch
batch_size=64,
# (float) The step size of gradient descent
learning_rate=0.001,
# (int) Frequence of target network update.
target_update_freq=100,
# (bool) Whether ignore done(usually for max step termination env).
# 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,
),
# collect_mode config
collect=dict(
# (int) How many training samples collected in one collection procedure.
# Only one of [n_sample, n_episode] shoule be set.
n_sample=4,
# (int) Split episodes or trajectories into pieces with length `unroll_len`.
unroll_len=1,
),
eval=dict(), # for compability
# other config
other=dict(
# Epsilon greedy with decay.
eps=dict(
# (str) Decay type. Support ['exp', 'linear'].
type='exp',
# (float) Epsilon start value.
start=0.95,
# (float) Epsilon end value.
end=0.1,
# (int) Decay length(env step).
decay=10000,
),
replay_buffer=dict(
# (int) Maximum size of replay buffer. Usually, larger buffer size is better.
replay_buffer_size=10000,
),
),
)
[docs] def _init_learn(self) -> None:
"""
Overview:
Initialize the learn mode of policy, including related attributes and modules. For MDQN, it contains \
optimizer, algorithm-specific arguments such as entropy_tau, m_alpha and nstep, 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._priority = self._cfg.priority
self._priority_IS_weight = self._cfg.priority_IS_weight
# Optimizer
# set eps in order to consistent with the original paper implementation
self._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate, eps=0.0003125)
self._gamma = self._cfg.discount_factor
self._nstep = self._cfg.nstep
self._entropy_tau = self._cfg.entropy_tau
self._m_alpha = self._cfg.m_alpha
# use model_wrapper for specialized demands of different modes
self._target_model = copy.deepcopy(self._model)
if 'target_update_freq' in self._cfg.learn:
self._target_model = model_wrap(
self._target_model,
wrapper_name='target',
update_type='assign',
update_kwargs={'freq': self._cfg.learn.target_update_freq}
)
elif 'target_theta' in self._cfg.learn:
self._target_model = model_wrap(
self._target_model,
wrapper_name='target',
update_type='momentum',
update_kwargs={'theta': self._cfg.learn.target_theta}
)
else:
raise RuntimeError("DQN needs target network, please either indicate target_update_freq or target_theta")
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: 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 replay buffer and then returns the output \
result, including various training information such as loss, action_gap, clip_frac, 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 MDQN, 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`` \
and ``value_gamma``.
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.
.. note::
For more detailed examples, please refer to our unittest for MDQNPolicy: ``ding.policy.tests.test_mdqn``.
"""
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=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)
q_value = self._learn_model.forward(data['obs'])['logit']
# Target q value
with torch.no_grad():
target_q_value_current = self._target_model.forward(data['obs'])['logit']
target_q_value = self._target_model.forward(data['next_obs'])['logit']
data_m = m_q_1step_td_data(
q_value, target_q_value_current, target_q_value, data['action'], data['reward'].squeeze(0), data['done'],
data['weight']
)
loss, td_error_per_sample, action_gap, clipfrac = m_q_1step_td_error(
data_m, self._gamma, self._entropy_tau, self._m_alpha
)
# ====================
# 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(),
'q_value': q_value.mean().item(),
'target_q_value': target_q_value.mean().item(),
'priority': td_error_per_sample.abs().tolist(),
'action_gap': action_gap.item(),
'clip_frac': clipfrac.mean().item(),
}
[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_value', 'action_gap', 'clip_frac']