Source code for ding.policy.collaq
from typing import List, Dict, Any, Tuple, Union, Optional
from collections import namedtuple
import torch
import copy
from ding.torch_utils import to_device, RMSprop
from ding.rl_utils import v_1step_td_data, v_1step_td_error, get_train_sample
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY
from ding.utils.data import timestep_collate, default_collate, default_decollate
from .base_policy import Policy
from .common_utils import default_preprocess_learn
[docs]@POLICY_REGISTRY.register('collaq')
class CollaQPolicy(Policy):
r"""
Overview:
Policy class of CollaQ algorithm. CollaQ is a multi-agent reinforcement learning algorithm
Interface:
_init_learn, _data_preprocess_learn, _forward_learn, _reset_learn, _state_dict_learn, _load_state_dict_learn\
_init_collect, _forward_collect, _reset_collect, _process_transition, _init_eval, _forward_eval\
_reset_eval, _get_train_sample, default_model
Config:
== ==================== ======== ============== ======================================== =======================
ID Symbol Type Default Value Description Other(Shape)
== ==================== ======== ============== ======================================== =======================
1 ``type`` str collaq | RL policy register name, refer to | this arg is optional,
| registry ``POLICY_REGISTRY`` | a placeholder
2 ``cuda`` bool True | 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_`` bool False | Whether use Importance Sampling | IS weight
| ``IS_weight`` | Weight to correct biased update.
6 | ``learn.update_`` int 20 | 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
7 | ``learn.target_`` float 0.001 | Target network update momentum | between[0,1]
| ``update_theta`` | parameter.
8 | ``learn.discount`` float 0.99 | Reward's future discount factor, aka. | may be 1 when sparse
| ``_factor`` | gamma | reward env
9 | ``learn.collaq`` float 1.0 | The weight of collaq MARA loss
| ``_loss_weight``
== ==================== ======== ============== ======================================== =======================
"""
config = dict(
# (str) RL policy register name (refer to function "POLICY_REGISTRY").
type='collaq',
# (bool) Whether to use cuda for network.
cuda=True,
# (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,
# (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.
priority_IS_weight=False,
learn=dict(
# (int) Collect n_episode data, update_model n_iteration times
update_per_collect=20,
# (int) The number of data for a train iteration
batch_size=32,
# (float) Gradient-descent step size
learning_rate=0.0005,
# ==============================================================
# The following configs is algorithm-specific
# ==============================================================
# (float) Target network update weight, theta * new_w + (1 - theta) * old_w, defaults in [0, 0.1]
target_update_theta=0.001,
# (float) Discount factor for future reward, defaults int [0, 1]
discount_factor=0.99,
# (float) The weight of collaq MARA loss
collaq_loss_weight=1.0,
# (float)
clip_value=100,
# (bool) Whether to use double DQN mechanism(target q for surpassing over estimation)
double_q=False,
),
collect=dict(
# (int) Only one of [n_sample, n_episode] shoule be set
# n_episode=32,
# (int) Cut trajectories into pieces with length "unroll_len", the length of timesteps
# in each forward when training. In qmix, it is greater than 1 because there is RNN.
unroll_len=10,
),
eval=dict(),
other=dict(
eps=dict(
# (str) Type of epsilon decay
type='exp',
# (float) Start value for epsilon decay, in [0, 1].
# 0 means not use epsilon decay.
start=1,
# (float) Start value for epsilon decay, in [0, 1].
end=0.05,
# (int) Decay length(env step)
decay=200000,
),
replay_buffer=dict(
# (int) max size of replay buffer
replay_buffer_size=5000,
max_reuse=10,
),
),
)
def default_model(self) -> Tuple[str, List[str]]:
"""
Overview:
Return this algorithm default model setting for demonstration.
Returns:
- model_info (:obj:`Tuple[str, List[str]]`): model name and mode import_names
.. note::
The user can define and use customized network model but must obey the same inferface definition indicated \
by import_names path. For collaq, ``ding.model.collaq.CollaQ`` .
"""
return 'collaq', ['ding.model.template.collaq']
def _init_learn(self) -> None:
"""
Overview:
Learn mode init method. Called by ``self.__init__``.
Init the learner model of CollaQPolicy
Arguments:
.. note::
The _init_learn method takes the argument from the self._cfg.learn in the config file
- learning_rate (:obj:`float`): The learning rate fo the optimizer
- gamma (:obj:`float`): The discount factor
- alpha (:obj:`float`): The collaQ loss factor, the weight for calculating MARL loss
- agent_num (:obj:`int`): Since this is a multi-agent algorithm, we need to input the agent num.
- batch_size (:obj:`int`): Need batch size info to init hidden_state plugins
"""
self._priority = self._cfg.priority
self._priority_IS_weight = self._cfg.priority_IS_weight
self._optimizer = RMSprop(
params=self._model.parameters(), lr=self._cfg.learn.learning_rate, alpha=0.99, eps=0.00001
)
self._gamma = self._cfg.learn.discount_factor
self._alpha = self._cfg.learn.collaq_loss_weight
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_update_theta}
)
self._target_model = model_wrap(
self._target_model,
wrapper_name='hidden_state',
state_num=self._cfg.learn.batch_size,
init_fn=lambda: [[None for _ in range(self._cfg.model.agent_num)] for _ in range(3)]
)
self._learn_model = model_wrap(
self._model,
wrapper_name='hidden_state',
state_num=self._cfg.learn.batch_size,
init_fn=lambda: [[None for _ in range(self._cfg.model.agent_num)] for _ in range(3)]
)
self._learn_model.reset()
self._target_model.reset()
def _data_preprocess_learn(
self,
data: List[Any],
use_priority_IS_weight: bool = False,
use_priority: bool = False,
) -> dict:
r"""
Overview:
Preprocess the data to fit the required data format for learning
Arguments:
- data (:obj:`List[Dict[str, Any]]`): the data collected from collect function
Returns:
- data (:obj:`Dict[str, Any]`): the processed data, from \
[len=B, ele={dict_key: [len=T, ele=Tensor(any_dims)]}] -> {dict_key: Tensor([T, B, any_dims])}
"""
# data preprocess
data = timestep_collate(data)
if self._cuda:
data = to_device(data, self._device)
if use_priority_IS_weight:
assert use_priority, "Use IS Weight correction, but Priority is not used."
if use_priority and use_priority_IS_weight:
if 'priority_IS' in data:
data['weight'] = data['priority_IS']
else: # for compability
data['weight'] = data['IS']
else:
data['weight'] = data.get('weight', None)
data['done'] = data['done'].float()
return data
def _forward_learn(self, data: dict) -> Dict[str, Any]:
r"""
Overview:
Forward and backward function of learn mode.
Arguments:
- data (:obj:`Dict[str, Any]`): Dict type data, a batch of data for training, values are torch.Tensor or \
np.ndarray or dict/list combinations.
Returns:
- info_dict (:obj:`Dict[str, Any]`): Dict type data, a info dict indicated training result, which will be \
recorded in text log and tensorboard, values are python scalar or a list of scalars.
ArgumentsKeys:
- necessary: ``obs``, ``next_obs``, ``action``, ``reward``, ``weight``, ``prev_state``, ``done``
ReturnsKeys:
- necessary: ``cur_lr``, ``total_loss``
- cur_lr (:obj:`float`): Current learning rate
- total_loss (:obj:`float`): The calculated loss
"""
data = self._data_preprocess_learn(data, self.cfg.priority_IS_weight, self.cfg.priority)
# ====================
# CollaQ forward
# ====================
self._learn_model.train()
self._target_model.train()
# for hidden_state plugin, we need to reset the main model and target model
self._learn_model.reset(state=data['prev_state'][0])
self._target_model.reset(state=data['prev_state'][0])
inputs = {'obs': data['obs'], 'action': data['action']}
ret = self._learn_model.forward(inputs, single_step=False)
total_q = ret['total_q']
agent_colla_alone_q = ret['agent_colla_alone_q'].sum(-1).sum(-1)
if self._cfg.learn.double_q:
next_inputs = {'obs': data['next_obs']}
logit_detach = self._learn_model.forward(next_inputs, single_step=False)['logit'].clone().detach()
next_inputs = {'obs': data['next_obs'], 'action': logit_detach.argmax(dim=-1)}
else:
next_inputs = {'obs': data['next_obs']}
with torch.no_grad():
target_total_q = self._target_model.forward(next_inputs, single_step=False)['total_q']
# td_loss calculation
td_data = v_1step_td_data(total_q, target_total_q, data['reward'], data['done'], data['weight'])
td_loss, td_error_per_sample = v_1step_td_error(td_data, self._gamma)
# collaQ loss calculation
colla_loss = (agent_colla_alone_q ** 2).mean()
# combine loss with factor
loss = colla_loss * self._alpha + td_loss
# ====================
# CollaQ update
# ====================
self._optimizer.zero_grad()
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(self._model.parameters(), self._cfg.learn.clip_value)
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(),
'colla_loss': colla_loss.item(),
'td_loss': td_loss.item(),
'grad_norm': grad_norm,
'priority': torch.mean(td_error_per_sample.abs(), dim=0).tolist(),
}
def _reset_learn(self, data_id: Optional[List[int]] = None) -> None:
r"""
Overview:
Reset learn model to the state indicated by data_id
Arguments:
- data_id (:obj:`Optional[List[int]]`): The id that store the state and we will reset\
the model state to the state indicated by data_id
"""
self._learn_model.reset(data_id=data_id)
def _state_dict_learn(self) -> Dict[str, Any]:
r"""
Overview:
Return the state_dict of learn mode, usually including model and optimizer.
Returns:
- state_dict (:obj:`Dict[str, Any]`): the dict of current policy learn state, for saving and restoring.
"""
return {
'model': self._learn_model.state_dict(),
'target_model': self._target_model.state_dict(),
'optimizer': self._optimizer.state_dict(),
}
def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None:
r"""
Overview:
Load the state_dict variable into policy learn mode.
Arguments:
- state_dict (:obj:`Dict[str, Any]`): the dict of policy learn state saved before.
.. tip::
If you want to only load some parts of model, you can simply set the ``strict`` argument in \
load_state_dict to ``False``, or refer to ``ding.torch_utils.checkpoint_helper`` for more \
complicated operation.
"""
self._learn_model.load_state_dict(state_dict['model'])
self._target_model.load_state_dict(state_dict['target_model'])
self._optimizer.load_state_dict(state_dict['optimizer'])
def _init_collect(self) -> None:
r"""
Overview:
Collect mode init method. Called by ``self.__init__``.
Init traj and unroll length, collect model.
Enable the eps_greedy_sample and the hidden_state plugin.
"""
self._unroll_len = self._cfg.collect.unroll_len
self._collect_model = model_wrap(
self._model,
wrapper_name='hidden_state',
state_num=self._cfg.collect.env_num,
save_prev_state=True,
init_fn=lambda: [[None for _ in range(self._cfg.model.agent_num)] for _ in range(3)]
)
self._collect_model = model_wrap(self._collect_model, wrapper_name='eps_greedy_sample')
self._collect_model.reset()
def _forward_collect(self, data: dict, eps: float) -> dict:
r"""
Overview:
Forward function for collect mode with eps_greedy
Arguments:
- data (:obj:`Dict[str, Any]`): Dict type data, stacked env data for predicting policy_output(action), \
values are torch.Tensor or np.ndarray or dict/list combinations, keys are env_id indicated by integer.
- eps (:obj:`float`): epsilon value for exploration, which is decayed by collected env step.
Returns:
- output (:obj:`Dict[int, Any]`): Dict type data, including at least inferred action according to input obs.
ReturnsKeys
- necessary: ``action``
"""
data_id = list(data.keys())
data = default_collate(list(data.values()))
if self._cuda:
data = to_device(data, self._device)
data = {'obs': data}
self._collect_model.eval()
with torch.no_grad():
output = self._collect_model.forward(data, eps=eps, data_id=data_id)
if self._cuda:
output = to_device(output, 'cpu')
output = default_decollate(output)
return {i: d for i, d in zip(data_id, output)}
def _reset_collect(self, data_id: Optional[List[int]] = None) -> None:
r"""
Overview:
Reset collect model to the state indicated by data_id
Arguments:
- data_id (:obj:`Optional[List[int]]`): The id that store the state and we will reset\
the model state to the state indicated by data_id
"""
self._collect_model.reset(data_id=data_id)
def _process_transition(self, obs: Any, model_output: dict, timestep: namedtuple) -> dict:
r"""
Overview:
Generate dict type transition data from inputs.
Arguments:
- obs (:obj:`Any`): Env observation
- model_output (:obj:`dict`): Output of collect model, including at least \
['action', 'prev_state', 'agent_colla_alone_q']
- timestep (:obj:`namedtuple`): Output after env step, including at least ['obs', 'reward', 'done']\
(here 'obs' indicates obs after env step).
Returns:
- transition (:obj:`dict`): Dict type transition data.
"""
transition = {
'obs': obs,
'next_obs': timestep.obs,
'prev_state': model_output['prev_state'],
'action': model_output['action'],
'agent_colla_alone_q': model_output['agent_colla_alone_q'],
'reward': timestep.reward,
'done': timestep.done,
}
return transition
def _init_eval(self) -> None:
r"""
Overview:
Evaluate mode init method. Called by ``self.__init__``.
Init eval model with argmax strategy and the hidden_state plugin.
"""
self._eval_model = model_wrap(
self._model,
wrapper_name='hidden_state',
state_num=self._cfg.eval.env_num,
save_prev_state=True,
init_fn=lambda: [[None for _ in range(self._cfg.model.agent_num)] for _ in range(3)]
)
self._eval_model = model_wrap(self._eval_model, wrapper_name='argmax_sample')
self._eval_model.reset()
def _forward_eval(self, data: dict) -> dict:
r"""
Overview:
Forward function for eval mode, similar to ``self._forward_collect``.
Arguments:
- data (:obj:`Dict[str, Any]`): Dict type data, stacked env data for predicting policy_output(action), \
values are torch.Tensor or np.ndarray or dict/list combinations, keys are env_id indicated by integer.
Returns:
- output (:obj:`Dict[int, Any]`): The dict of predicting action for the interaction with env.
ReturnsKeys
- necessary: ``action``
"""
data_id = list(data.keys())
data = default_collate(list(data.values()))
if self._cuda:
data = to_device(data, self._device)
data = {'obs': data}
self._eval_model.eval()
with torch.no_grad():
output = self._eval_model.forward(data, data_id=data_id)
if self._cuda:
output = to_device(output, 'cpu')
output = default_decollate(output)
return {i: d for i, d in zip(data_id, output)}
def _reset_eval(self, data_id: Optional[List[int]] = None) -> None:
r"""
Overview:
Reset eval model to the state indicated by data_id
Arguments:
- data_id (:obj:`Optional[List[int]]`): The id that store the state and we will reset\
the model state to the state indicated by data_id
"""
self._eval_model.reset(data_id=data_id)
def _get_train_sample(self, data: list) -> Union[None, List[Any]]:
r"""
Overview:
Get the train sample from trajectory.
Arguments:
- data (:obj:`list`): The trajectory's cache
Returns:
- samples (:obj:`dict`): The training samples generated
"""
return get_train_sample(data, self._unroll_len)
def _monitor_vars_learn(self) -> List[str]:
r"""
Overview:
Return variables' name if variables are to used in monitor.
Returns:
- vars (:obj:`List[str]`): Variables' name list.
"""
return ['cur_lr', 'total_loss', 'colla_loss', 'td_loss', 'grad_norm']