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from collections import namedtuple
from typing import List, Dict, Any, Tuple
import torch
import treetensor.torch as ttorch
from ding.model import model_wrap
from ding.rl_utils import vtrace_data, vtrace_error_discrete_action, vtrace_error_continuous_action, 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, ttorch_collate
from ding.policy.base_policy import Policy
@POLICY_REGISTRY.register('impala')
class IMPALAPolicy(Policy):
"""
Overview:
Policy class of IMPALA algorithm. Paper link: https://arxiv.org/abs/1802.01561.
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
== ==================== ======== ============== ======================================== =======================
"""
config = dict(
# (str) RL policy register name (refer to function "POLICY_REGISTRY").
type='impala',
# (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 use Importance Sampling Weight to correct biased update. If True, priority must be True.
priority_IS_weight=False,
# (str) Which kind of action space used in IMPALAPolicy, ['discrete', 'continuous'].
action_space='discrete',
# (int) the trajectory length to calculate v-trace target.
unroll_len=32,
# (bool) Whether to need policy data in process transition.
transition_with_policy_data=True,
# learn_mode config
learn=dict(
# (int) collect n_sample data, train model update_per_collect times.
update_per_collect=4,
# (int) the number of data for a train iteration.
batch_size=16,
# (float) The step size of gradient descent.
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=0.99,
# (float) additional discounting parameter.
lambda_=0.95,
# (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,
# (str) The gradient clip operation type used in IMPALA, ['clip_norm', clip_value', 'clip_momentum_norm'].
grad_clip_type=None,
# (float) The gradient clip target value used in IMPALA.
# If ``grad_clip_type`` is 'clip_norm', then the maximum of gradient will be normalized to this value.
clip_value=0.5,
# (str) Optimizer used to train the network, ['adam', 'rmsprop'].
optim='adam',
),
# 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=16,
),
eval=dict(), # for compatibility
other=dict(
replay_buffer=dict(
# (int) Maximum size of replay buffer. Usually, larger buffer size is better.
replay_buffer_size=1000,
# (int) Maximum use times for a sample in buffer. If reaches this value, the sample will be removed.
max_use=16,
),
),
)
def default_model(self) -> Tuple[str, List[str]]:
"""
Overview:
Return this algorithm default neural network model setting for demonstration. ``__init__`` method will \
automatically call this method to get the default model setting and create model.
Returns:
- model_info (:obj:`Tuple[str, List[str]]`): The registered model name and model's 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 example about IMPALA , its registered name is ``vac`` and the import_names is \
``ding.model.template.vac``.
"""
return 'vac', ['ding.model.template.vac']
def _init_learn(self) -> None:
"""
Overview:
Initialize the learn mode of policy, including related attributes and modules. For IMPALA, it mainly \
contains optimizer, algorithm-specific arguments such as loss weight and gamma, main (learn) 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``.
"""
assert self._cfg.action_space in ["continuous", "discrete"], self._cfg.action_space
self._action_space = self._cfg.action_space
# Optimizer
optim_type = self._cfg.learn.optim
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=self._cfg.learn.grad_clip_type,
clip_value=self._cfg.learn.clip_value,
lr=self._cfg.learn.learning_rate
)
else:
raise NotImplementedError("Now only support rmsprop and adam, but input is {}".format(optim_type))
self._learn_model = model_wrap(self._model, wrapper_name='base')
self._action_shape = self._cfg.model.action_shape
self._unroll_len = self._cfg.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)`
"""
elem = data[0]
if isinstance(elem, dict): # old pipeline
data = default_collate(data)
elif isinstance(elem, list): # new task pipeline
data = default_collate(default_collate(data))
else:
raise TypeError("not support element type ({}) in IMPALA".format(type(elem)))
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)
if isinstance(elem, dict): # old pipeline
for k in data:
if isinstance(data[k], list):
data[k] = default_collate(data[k])
data['obs_plus_1'] = torch.cat([data['obs'], data['next_obs'][-1:]], dim=0) # shape (T+1)*B,env_obs_shape
return data
def _forward_learn(self, data: List[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 and current learning rate.
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 IMPALA, each element in list is a dict containing at least the following keys: ``obs``, \
``action``, ``logit``, ``reward``, ``next_obs``, ``done``. Sometimes, it also contains other keys such \
as ``weight``.
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 unittest for IMPALAPolicy: ``ding.policy.tests.test_impala``.
"""
data = self._data_preprocess_learn(data)
# ====================
# IMPALA forward
# ====================
self._learn_model.train()
output = self._learn_model.forward(
data['obs_plus_1'].view((-1, ) + data['obs_plus_1'].shape[2:]), 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
if self._action_space == 'continuous':
vtrace_loss = vtrace_error_continuous_action(data, g, l, r, c, rg)
elif self._action_space == 'discrete':
vtrace_loss = vtrace_error_discrete_action(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:
"""
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)`
"""
if self._action_space == 'continuous':
target_logit = {}
target_logit['mu'] = output['logit']['mu'].reshape(self._unroll_len + 1, -1,
self._action_shape)[:-1
] # shape (T+1),B,env_action_shape
target_logit['sigma'] = output['logit']['sigma'].reshape(self._unroll_len + 1, -1, self._action_shape
)[:-1] # shape (T+1),B,env_action_shape
elif self._action_space == 'discrete':
target_logit = output['logit'].reshape(self._unroll_len + 1, -1,
self._action_shape)[:-1] # shape (T+1),B,env_action_shape
behaviour_logit = data['logit'] # shape T,B
actions = data['action'] # shape T,B for discrete # shape T,B,env_action_shape for continuous
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'].float() # 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 _init_collect(self) -> None:
"""
Overview:
Initialize the collect mode of policy, including related attributes and modules. For IMPALA, it contains \
the collect_model to balance the exploration and exploitation (e.g. the multinomial sample mechanism in \
discrete action space), and other algorithm-specific arguments such as unroll_len.
This method will be called in ``__init__`` method if ``collect`` field is in ``enable_field``.
.. note::
If you want to set some spacial member variables in ``_init_collect`` method, you'd better name them \
with prefix ``_collect_`` to avoid conflict with other modes, such as ``self._collect_attr1``.
"""
assert self._cfg.action_space in ["continuous", "discrete"]
self._action_space = self._cfg.action_space
if self._action_space == 'continuous':
self._collect_model = model_wrap(self._model, wrapper_name='reparam_sample')
elif self._action_space == 'discrete':
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, Any]:
"""
Overview:
Policy forward function of collect mode (collecting training data by interacting with envs). Forward means \
that the policy gets some necessary data (mainly observation) from the envs and then returns the output \
data, such as the action to interact with the envs.
Arguments:
- data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \
key of the dict is environment id and the value is the corresponding data of the env.
Returns:
- output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action and \
other necessary data (action logit and value) for learn mode defined in ``self._process_transition`` \
method. The key of the dict is the same as the input data, i.e. environment id.
.. tip::
If you want to add more tricks on this policy, like temperature factor in multinomial sample, you can pass \
related data as extra keyword arguments of this 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 unittest for IMPALAPolicy: ``ding.policy.tests.test_impala``.
"""
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]]:
"""
Overview:
For a given trajectory (transitions, a list of transition) data, process it into a list of sample that \
can be used for training. In IMPALA, a train sample is processed transitions with unroll_len length.
Arguments:
- transitions (: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:`List[Dict[str, Any]]`): The processed train samples, each element is the similar format \
as input transitions, but may contain more data for training.
"""
return get_train_sample(data, self._unroll_len)
def _process_transition(self, obs: torch.Tensor, policy_output: Dict[str, torch.Tensor],
timestep: namedtuple) -> Dict[str, torch.Tensor]:
"""
Overview:
Process and pack one timestep transition data into a dict, which can be directly used for training and \
saved in replay buffer. For IMPALA, it contains obs, next_obs, action, reward, done, logit.
Arguments:
- obs (:obj:`torch.Tensor`): The env observation of current timestep, such as stacked 2D image in Atari.
- policy_output (:obj:`Dict[str, torch.Tensor]`): The output of the policy network with the observation \
as input. For IMPALA, it contains the action and the logit of the action.
- timestep (:obj:`namedtuple`): The execution result namedtuple returned by the environment step method, \
except all the elements have been transformed into tensor data. Usually, it contains the next obs, \
reward, done, info, etc.
Returns:
- transition (:obj:`Dict[str, torch.Tensor]`): The processed transition data of the current timestep.
"""
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:
"""
Overview:
Initialize the eval mode of policy, including related attributes and modules. For IMPALA, it contains the \
eval model to select optimial action (e.g. greedily select action with argmax mechanism in discrete action).
This method will be called in ``__init__`` method if ``eval`` field is in ``enable_field``.
.. note::
If you want to set some spacial member variables in ``_init_eval`` method, you'd better name them \
with prefix ``_eval_`` to avoid conflict with other modes, such as ``self._eval_attr1``.
"""
assert self._cfg.action_space in ["continuous", "discrete"], self._cfg.action_space
self._action_space = self._cfg.action_space
if self._action_space == 'continuous':
self._eval_model = model_wrap(self._model, wrapper_name='deterministic_sample')
elif self._action_space == 'discrete':
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]:
"""
Overview:
Policy forward function of eval mode (evaluation policy performance by interacting with envs). Forward \
means that the policy gets some necessary data (mainly observation) from the envs and then returns the \
action to interact with the envs. ``_forward_eval`` in IMPALA often uses deterministic sample to get \
actions while ``_forward_collect`` usually uses stochastic sample method for balance exploration and \
exploitation.
Arguments:
- data (:obj:`Dict[int, Any]`): The input data used for policy forward, including at least the obs. The \
key of the dict is environment id and the value is the corresponding data of the env.
Returns:
- output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action. The \
key of the dict is the same as the input data, i.e. environment id.
.. 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 unittest for IMPALAPolicy: ``ding.policy.tests.test_impala``.
"""
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]:
"""
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 super()._monitor_vars_learn() + ['policy_loss', 'value_loss', 'entropy_loss']
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