Source code for ding.policy.acer
from collections import namedtuple
from typing import List, Dict, Any, Tuple
import copy
import torch
from ding.model import model_wrap
from ding.rl_utils import get_train_sample, compute_q_retraces, acer_policy_error,\
acer_value_error, acer_trust_region_update
from ding.torch_utils import Adam, RMSprop, to_device
from ding.utils import POLICY_REGISTRY
from ding.utils.data import default_collate, default_decollate
from ding.policy.base_policy import Policy
EPS = 1e-8
[docs]@POLICY_REGISTRY.register('acer')
class ACERPolicy(Policy):
r"""
Overview:
Policy class of ACER algorithm.
Config:
== ======================= ======== ============== ===================================== =======================
ID Symbol Type Default Value Description Other(Shape)
== ======================= ======== ============== ===================================== =======================
1 ``type`` str acer | 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 ``trust_region`` bool True | Whether the RL algorithm use trust |
| region constraint |
5 ``trust_region_value`` float 1.0 | maximum range of the trust region |
6 ``unroll_len`` int 32 | trajectory length to calculate
| Q retrace target
7 ``learn.update`` int 4 | How many updates(iterations) to | this args can be vary
``per_collect`` | train after collector's one | from envs. Bigger val
| collection. Only |
| valid in serial training | means more off-policy
8 ``c_clip_ratio`` float 1.0 | clip ratio of importance weights |
== ======================= ======== ============== ===================================== =======================
"""
unroll_len = 32
config = dict(
type='acer',
cuda=False,
# (bool) whether use on-policy training pipeline(behaviour policy and training policy are the same)
# here we follow ppo serial pipeline, the original is False
on_policy=False,
priority=False,
# (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.
priority_IS_weight=False,
learn=dict(
# (str) the type of gradient clip method
grad_clip_type=None,
# (float) max value when ACER use gradient clip
clip_value=None,
# (int) collect n_sample data, train model update_per_collect times
# here we follow ppo serial pipeline
update_per_collect=4,
# (int) the number of data for a train iteration
batch_size=16,
# (float) loss weight of the value network, the weight of policy network is set to 1
value_weight=0.5,
# (float) loss weight of the entropy regularization, the weight of policy network is set to 1
entropy_weight=0.0001,
# (float) discount factor for future reward, defaults int [0, 1]
discount_factor=0.9,
# (float) additional discounting parameter
lambda_=0.95,
# (int) the trajectory length to calculate v-trace target
unroll_len=unroll_len,
# (float) clip ratio of importance weights
c_clip_ratio=10,
trust_region=True,
trust_region_value=1.0,
learning_rate_actor=0.0005,
learning_rate_critic=0.0005,
target_theta=0.01
),
collect=dict(
# (int) collect n_sample data, train model n_iteration times
# n_sample=16,
# (int) the trajectory length to calculate v-trace target
unroll_len=unroll_len,
# (float) discount factor for future reward, defaults int [0, 1]
discount_factor=0.9,
gae_lambda=0.95,
collector=dict(
type='sample',
collect_print_freq=1000,
),
),
eval=dict(evaluator=dict(eval_freq=200, ), ),
other=dict(replay_buffer=dict(
replay_buffer_size=1000,
max_use=16,
), ),
)
def default_model(self) -> Tuple[str, List[str]]:
return 'acer', ['ding.model.template.acer']
def _init_learn(self) -> None:
r"""
Overview:
Learn mode init method. Called by ``self.__init__``.
Initialize the optimizer, algorithm config and main model.
"""
# Optimizer
self._optimizer_actor = Adam(
self._model.actor.parameters(),
lr=self._cfg.learn.learning_rate_actor,
grad_clip_type=self._cfg.learn.grad_clip_type,
clip_value=self._cfg.learn.clip_value
)
self._optimizer_critic = Adam(
self._model.critic.parameters(),
lr=self._cfg.learn.learning_rate_critic,
)
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._action_shape = self._cfg.model.action_shape
self._unroll_len = self._cfg.learn.unroll_len
# Algorithm config
self._priority = self._cfg.priority
self._priority_IS_weight = self._cfg.priority_IS_weight
self._value_weight = self._cfg.learn.value_weight
self._entropy_weight = self._cfg.learn.entropy_weight
self._gamma = self._cfg.learn.discount_factor
# self._rho_clip_ratio = self._cfg.learn.rho_clip_ratio
self._c_clip_ratio = self._cfg.learn.c_clip_ratio
# self._rho_pg_clip_ratio = self._cfg.learn.rho_pg_clip_ratio
self._use_trust_region = self._cfg.learn.trust_region
self._trust_region_value = self._cfg.learn.trust_region_value
# Main model
self._learn_model.reset()
self._target_model.reset()
def _data_preprocess_learn(self, data: List[Dict[str, Any]]):
"""
Overview:
Data preprocess function of learn mode.
Convert list trajectory data to to trajectory data, which is a dict of tensors.
Arguments:
- data (:obj:`List[Dict[str, Any]]`): List type data, a list of data for training. Each list element is a \
dict, whose values are torch.Tensor or np.ndarray or dict/list combinations, keys include at least 'obs',\
'next_obs', 'logit', 'action', 'reward', 'done'
Returns:
- data (:obj:`dict`): Dict type data. Values are torch.Tensor or np.ndarray or dict/list combinations. \
ReturnsKeys:
- necessary: 'logit', 'action', 'reward', 'done', 'weight', 'obs_plus_1'.
- optional and not used in later computation: 'obs', 'next_obs'.'IS', 'collect_iter', 'replay_unique_id', \
'replay_buffer_idx', 'priority', 'staleness', 'use'.
ReturnsShapes:
- obs_plus_1 (:obj:`torch.FloatTensor`): :math:`(T * B, obs_shape)`, where T is timestep, B is batch size \
and obs_shape is the shape of single env observation
- logit (:obj:`torch.FloatTensor`): :math:`(T, B, N)`, where N is action dim
- action (:obj:`torch.LongTensor`): :math:`(T, B)`
- reward (:obj:`torch.FloatTensor`): :math:`(T+1, B)`
- done (:obj:`torch.FloatTensor`): :math:`(T, B)`
- weight (:obj:`torch.FloatTensor`): :math:`(T, B)`
"""
data = default_collate(data)
if self._cuda:
data = to_device(data, self._device)
data['weight'] = data.get('weight', None)
# shape (T+1)*B,env_obs_shape
data['obs_plus_1'] = torch.cat((data['obs'] + data['next_obs'][-1:]), dim=0)
data['logit'] = torch.cat(
data['logit'], dim=0
).reshape(self._unroll_len, -1, self._action_shape) # shape T,B,env_action_shape
data['action'] = torch.cat(data['action'], dim=0).reshape(self._unroll_len, -1) # shape T,B,
data['done'] = torch.cat(data['done'], dim=0).reshape(self._unroll_len, -1).float() # shape T,B,
data['reward'] = torch.cat(data['reward'], dim=0).reshape(self._unroll_len, -1) # shape T,B,
data['weight'] = torch.cat(
data['weight'], dim=0
).reshape(self._unroll_len, -1) if data['weight'] else None # shape T,B
return data
def _forward_learn(self, data: List[Dict[str, Any]]) -> Dict[str, Any]:
r"""
Overview:
Forward computation graph of learn mode(updating policy).
Arguments:
- data (:obj:`List[Dict[str, Any]]`): List type data, a list of data for training. Each list element is a \
dict, whose values are torch.Tensor or np.ndarray or dict/list combinations, keys include at least 'obs',\
'next_obs', 'logit', 'action', 'reward', 'done'
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``, ``action``, ``reward``, ``next_obs``, ``done``
- optional: 'collect_iter', 'replay_unique_id', 'replay_buffer_idx', 'priority', 'staleness', 'use', 'IS'
ReturnsKeys:
- necessary: ``cur_lr_actor``, ``cur_lr_critic``, ``actor_loss`,``bc_loss``,``policy_loss``,\
``critic_loss``,``entropy_loss``
"""
data = self._data_preprocess_learn(data)
self._learn_model.train()
action_data = self._learn_model.forward(data['obs_plus_1'], mode='compute_actor')
q_value_data = self._learn_model.forward(data['obs_plus_1'], mode='compute_critic')
avg_action_data = self._target_model.forward(data['obs_plus_1'], mode='compute_actor')
target_logit, behaviour_logit, avg_logit, actions, q_values, rewards, weights = self._reshape_data(
action_data, avg_action_data, q_value_data, data
)
# shape (T+1),B,env_action_shape
target_logit = torch.log_softmax(target_logit, dim=-1)
# shape T,B,env_action_shape
behaviour_logit = torch.log_softmax(behaviour_logit, dim=-1)
# shape (T+1),B,env_action_shape
avg_logit = torch.log_softmax(avg_logit, dim=-1)
with torch.no_grad():
# shape T,B,env_action_shape
ratio = torch.exp(target_logit[0:-1] - behaviour_logit)
# shape (T+1),B,1
v_pred = (q_values * torch.exp(target_logit)).sum(-1).unsqueeze(-1)
# Calculate retrace
q_retraces = compute_q_retraces(q_values, v_pred, rewards, actions, weights, ratio, self._gamma)
# the terminal states' weights are 0. it needs to be shift to count valid state
weights_ext = torch.ones_like(weights)
weights_ext[1:] = weights[0:-1]
weights = weights_ext
q_retraces = q_retraces[0:-1] # shape T,B,1
q_values = q_values[0:-1] # shape T,B,env_action_shape
v_pred = v_pred[0:-1] # shape T,B,1
target_logit = target_logit[0:-1] # shape T,B,env_action_shape
avg_logit = avg_logit[0:-1] # shape T,B,env_action_shape
total_valid = weights.sum() # 1
# ====================
# policy update
# ====================
actor_loss, bc_loss = acer_policy_error(
q_values, q_retraces, v_pred, target_logit, actions, ratio, self._c_clip_ratio
)
actor_loss = actor_loss * weights.unsqueeze(-1)
bc_loss = bc_loss * weights.unsqueeze(-1)
dist_new = torch.distributions.categorical.Categorical(logits=target_logit)
entropy_loss = (dist_new.entropy() * weights).unsqueeze(-1) # shape T,B,1
total_actor_loss = (actor_loss + bc_loss + self._entropy_weight * entropy_loss).sum() / total_valid
self._optimizer_actor.zero_grad()
actor_gradients = torch.autograd.grad(-total_actor_loss, target_logit, retain_graph=True)
if self._use_trust_region:
actor_gradients = acer_trust_region_update(
actor_gradients, target_logit, avg_logit, self._trust_region_value
)
target_logit.backward(actor_gradients)
self._optimizer_actor.step()
# ====================
# critic update
# ====================
critic_loss = (acer_value_error(q_values, q_retraces, actions) * weights.unsqueeze(-1)).sum() / total_valid
self._optimizer_critic.zero_grad()
critic_loss.backward()
self._optimizer_critic.step()
self._target_model.update(self._learn_model.state_dict())
with torch.no_grad():
kl_div = torch.exp(avg_logit) * (avg_logit - target_logit)
kl_div = (kl_div.sum(-1) * weights).sum() / total_valid
return {
'cur_actor_lr': self._optimizer_actor.defaults['lr'],
'cur_critic_lr': self._optimizer_critic.defaults['lr'],
'actor_loss': (actor_loss.sum() / total_valid).item(),
'bc_loss': (bc_loss.sum() / total_valid).item(),
'policy_loss': total_actor_loss.item(),
'critic_loss': critic_loss.item(),
'entropy_loss': (entropy_loss.sum() / total_valid).item(),
'kl_div': kl_div.item()
}
def _reshape_data(
self, action_data: Dict[str, Any], avg_action_data: Dict[str, Any], q_value_data: Dict[str, Any],
data: Dict[str, Any]
) -> Tuple[Any, Any, Any, Any, Any, Any]:
r"""
Overview:
Obtain weights for loss calculating, where should be 0 for done positions
Update values and rewards with the weight
Arguments:
- output (:obj:`Dict[int, Any]`): Dict type data, output of learn_model forward. \
Values are torch.Tensor or np.ndarray or dict/list combinations,keys are value, logit.
- data (:obj:`Dict[int, Any]`): Dict type data, input of policy._forward_learn \
Values are torch.Tensor or np.ndarray or dict/list combinations. Keys includes at \
least ['logit', 'action', 'reward', 'done',]
Returns:
- data (:obj:`Tuple[Any]`): Tuple of target_logit, behaviour_logit, actions, \
values, rewards, weights
ReturnsShapes:
- target_logit (:obj:`torch.FloatTensor`): :math:`((T+1), B, Obs_Shape)`, where T is timestep,\
B is batch size and Obs_Shape is the shape of single env observation.
- behaviour_logit (:obj:`torch.FloatTensor`): :math:`(T, B, N)`, where N is action dim.
- avg_action_logit (:obj:`torch.FloatTensor`): :math: `(T+1, B, N)`, where N is action dim.
- actions (:obj:`torch.LongTensor`): :math:`(T, B)`
- values (:obj:`torch.FloatTensor`): :math:`(T+1, B)`
- rewards (:obj:`torch.FloatTensor`): :math:`(T, B)`
- weights (:obj:`torch.FloatTensor`): :math:`(T, B)`
"""
target_logit = action_data['logit'].reshape(
self._unroll_len + 1, -1, self._action_shape
) # shape (T+1),B,env_action_shape
behaviour_logit = data['logit'] # shape T,B,env_action_shape
avg_action_logit = avg_action_data['logit'].reshape(
self._unroll_len + 1, -1, self._action_shape
) # shape (T+1),B,env_action_shape
actions = data['action'] # shape T,B
values = q_value_data['q_value'].reshape(
self._unroll_len + 1, -1, self._action_shape
) # shape (T+1),B,env_action_shape
rewards = data['reward'] # shape T,B
weights_ = 1 - data['done'] # shape T,B
weights = torch.ones_like(rewards) # shape T,B
weights = weights_
return target_logit, behaviour_logit, avg_action_logit, actions, values, rewards, weights
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(),
'actor_optimizer': self._optimizer_actor.state_dict(),
'critic_optimizer': self._optimizer_critic.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_actor.load_state_dict(state_dict['actor_optimizer'])
self._optimizer_critic.load_state_dict(state_dict['critic_optimizer'])
def _init_collect(self) -> None:
r"""
Overview:
Collect mode init method. Called by ``self.__init__``, initialize algorithm arguments and collect_model.
Use multinomial_sample to choose action.
"""
self._collect_unroll_len = self._cfg.collect.unroll_len
self._collect_model = model_wrap(self._model, wrapper_name='multinomial_sample')
self._collect_model.reset()
def _forward_collect(self, data: Dict[int, Any]) -> Dict[int, Dict[str, Any]]:
r"""
Overview:
Forward computation graph of collect mode(collect training data).
Arguments:
- data (:obj:`Dict[int, Any]`): Dict type data, stacked env data for predicting \
action, values are torch.Tensor or np.ndarray or dict/list combinations,keys \
are env_id indicated by integer.
Returns:
- output (:obj:`Dict[int, Dict[str,Any]]`): Dict of predicting policy_output(logit, action) for each env.
ReturnsKeys
- necessary: ``logit``, ``action``
"""
data_id = list(data.keys())
data = default_collate(list(data.values()))
if self._cuda:
data = to_device(data, self._device)
self._collect_model.eval()
with torch.no_grad():
output = self._collect_model.forward(data, mode='compute_actor')
if self._cuda:
output = to_device(output, 'cpu')
output = default_decollate(output)
output = {i: d for i, d in zip(data_id, output)}
return output
def _get_train_sample(self, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
r"""
Overview:
For a given trajectory(transitions, a list of transition) data, process it into a list of sample that \
can be used for training directly.
Arguments:
- data (:obj:`List[Dict[str, Any]`): The trajectory data(a list of transition), each element is the same \
format as the return value of ``self._process_transition`` method.
Returns:
- samples (:obj:`dict`): List of training samples.
.. note::
We will vectorize ``process_transition`` and ``get_train_sample`` method in the following release version. \
And the user can customize the this data processing procedure by overriding this two methods and collector \
itself.
"""
return get_train_sample(data, self._unroll_len)
def _process_transition(self, obs: Any, policy_output: Dict[str, Any], timestep: namedtuple) -> Dict[str, Any]:
r"""
Overview:
Generate dict type transition data from inputs.
Arguments:
- obs (:obj:`Any`): Env observation,can be torch.Tensor or np.ndarray or dict/list combinations.
- model_output (:obj:`dict`): Output of collect model, including ['logit','action']
- 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, including at least ['obs','next_obs', 'logit',\
'action','reward', 'done']
"""
transition = {
'obs': obs,
'next_obs': timestep.obs,
'logit': policy_output['logit'],
'action': policy_output['action'],
'reward': timestep.reward,
'done': timestep.done,
}
return transition
def _init_eval(self) -> None:
r"""
Overview:
Evaluate mode init method. Called by ``self.__init__``, initialize eval_model,
and use argmax_sample to choose action.
"""
self._eval_model = model_wrap(self._model, wrapper_name='argmax_sample')
self._eval_model.reset()
def _forward_eval(self, data: Dict[int, Any]) -> Dict[int, Any]:
r"""
Overview:
Forward computation graph of eval mode(evaluate policy performance), at most cases, it is 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``
- optional: ``logit``
"""
data_id = list(data.keys())
data = default_collate(list(data.values()))
if self._cuda:
data = to_device(data, self._device)
self._eval_model.eval()
with torch.no_grad():
output = self._eval_model.forward(data, mode='compute_actor')
if self._cuda:
output = to_device(output, 'cpu')
output = default_decollate(output)
output = {i: d for i, d in zip(data_id, output)}
return output
def _monitor_vars_learn(self) -> List[str]:
r"""
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 interface definition indicated \
by import_names path. For IMPALA, ``ding.model.interface.IMPALA``
"""
return ['actor_loss', 'bc_loss', 'policy_loss', 'critic_loss', 'entropy_loss', 'kl_div']