from collections import namedtuple
from typing import List, Dict, Any, Tuple
import torch
from ding.model import model_wrap
from ding.rl_utils import vtrace_data, vtrace_error, get_train_sample
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
[docs]@POLICY_REGISTRY.register('impala')
class IMPALAPolicy(Policy):
r"""
Overview:
Policy class of IMPALA algorithm.
Config:
== ==================== ======== ============== ======================================== =======================
ID Symbol Type Default Value Description Other(Shape)
== ==================== ======== ============== ======================================== =======================
1 ``type`` str impala | 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_`` bool False | Whether use Importance Sampling Weight | If True, priority
| ``IS_weight`` | | must be True
6 ``unroll_len`` int 32 | trajectory length to calculate v-trace
| target
7 | ``learn.update`` int 4 | 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
== ==================== ======== ============== ======================================== =======================
"""
unroll_len = 32
config = dict(
type='impala',
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(
# (bool) Whether to use multi gpu
multi_gpu=False,
# (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,
learning_rate=0.0005,
# (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
rho_clip_ratio=1.0,
# (float) clip ratio of importance weights
c_clip_ratio=1.0,
# (float) clip ratio of importance sampling
rho_pg_clip_ratio=1.0,
),
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(
type='priority',
replay_buffer_size=1000,
max_use=16,
), ),
)
def _init_learn(self) -> None:
r"""
Overview:
Learn mode init method. Called by ``self.__init__``.
Initialize the optimizer, algorithm config and main model.
"""
# Optimizer
grad_clip_type = self._cfg.learn.get("grad_clip_type", None)
clip_value = self._cfg.learn.get("clip_value", None)
optim_type = self._cfg.learn.get("optim", "adam")
if optim_type == 'rmsprop':
self._optimizer = RMSprop(self._model.parameters(), lr=self._cfg.learn.learning_rate)
elif optim_type == 'adam':
self._optimizer = Adam(
self._model.parameters(),
grad_clip_type=grad_clip_type,
clip_value=clip_value,
lr=self._cfg.learn.learning_rate
)
else:
raise NotImplementedError
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._lambda = self._cfg.learn.lambda_
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
# Main model
self._learn_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)
if self._priority_IS_weight:
assert self._priority, "Use IS Weight correction, but Priority is not used."
if self._priority and self._priority_IS_weight:
data['weight'] = data['IS']
else:
data['weight'] = data.get('weight', None)
data['obs_plus_1'] = torch.cat((data['obs'] + data['next_obs'][-1:]), dim=0) # shape (T+1)*B,env_obs_shape
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``, ``total_loss``, ``policy_loss`,``value_loss``,``entropy_loss``
- optional: ``priority``
"""
data = self._data_preprocess_learn(data)
# ====================
# IMPALA forward
# ====================
self._learn_model.train()
output = self._learn_model.forward(data['obs_plus_1'], mode='compute_actor_critic')
target_logit, behaviour_logit, actions, values, rewards, weights = self._reshape_data(output, data)
# Calculate vtrace error
data = vtrace_data(target_logit, behaviour_logit, actions, values, rewards, weights)
g, l, r, c, rg = self._gamma, self._lambda, self._rho_clip_ratio, self._c_clip_ratio, self._rho_pg_clip_ratio
vtrace_loss = vtrace_error(data, g, l, r, c, rg)
wv, we = self._value_weight, self._entropy_weight
total_loss = vtrace_loss.policy_loss + wv * vtrace_loss.value_loss - we * vtrace_loss.entropy_loss
# ====================
# IMPALA update
# ====================
self._optimizer.zero_grad()
total_loss.backward()
self._optimizer.step()
return {
'cur_lr': self._optimizer.defaults['lr'],
'total_loss': total_loss.item(),
'policy_loss': vtrace_loss.policy_loss.item(),
'value_loss': vtrace_loss.value_loss.item(),
'entropy_loss': vtrace_loss.entropy_loss.item(),
}
def _reshape_data(self, output: 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.
- 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 = output['logit'].reshape(self._unroll_len + 1, -1,
self._action_shape)[:-1] # shape (T+1),B,env_obs_shape
behaviour_logit = data['logit'] # shape T,B
actions = data['action'] # shape T,B
values = output['value'].reshape(self._unroll_len + 1, -1) # 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
values[1:] = values[1:] * weights_
weights[1:] = weights_[:-1]
rewards = rewards * weights # shape T,B
return target_logit, behaviour_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(),
'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._optimizer.load_state_dict(state_dict['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 default_model(self) -> Tuple[str, List[str]]:
return 'vac', ['ding.model.template.vac']
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 super()._monitor_vars_learn() + ['policy_loss', 'value_loss', 'entropy_loss']