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import copy
from collections import namedtuple
from typing import List, Dict, Any, Tuple, Union, Optional
import torch
from ding.model import model_wrap
from ding.rl_utils import q_nstep_td_data, q_nstep_td_error, q_nstep_td_error_with_rescale, get_nstep_return_data, \
get_train_sample
from ding.torch_utils import Adam, to_device
from ding.utils import POLICY_REGISTRY
from ding.utils.data import timestep_collate, default_collate, default_decollate
from .base_policy import Policy
@POLICY_REGISTRY.register('r2d2')
class R2D2Policy(Policy):
"""
Overview:
Policy class of R2D2, from paper `Recurrent Experience Replay in Distributed Reinforcement Learning` .
R2D2 proposes that several tricks should be used to improve upon DRQN, namely some recurrent experience replay \
tricks and the burn-in mechanism for off-policy training.
Config:
== ==================== ======== ============== ======================================== =======================
ID Symbol Type Default Value Description Other(Shape)
== ==================== ======== ============== ======================================== =======================
1 ``type`` str r2d2 | 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 0.997, | Reward's future discount factor, aka. | May be 1 when sparse
| ``factor`` [0.95, 0.999] | gamma | reward env
7 ``nstep`` int 3, | N-step reward discount sum for target
[3, 5] | q_value estimation
8 ``burnin_step`` int 2 | The timestep of burnin operation,
| which is designed to RNN hidden state
| difference caused by off-policy
9 | ``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
10 | ``learn.batch_`` int 64 | The number of samples of an iteration
| ``size``
11 | ``learn.learning`` float 0.001 | Gradient step length of an iteration.
| ``_rate``
12 | ``learn.value_`` bool True | Whether use value_rescale function for
| ``rescale`` | predicted value
13 | ``learn.target_`` int 100 | Frequence of target network update. | Hard(assign) update
| ``update_freq``
14 | ``learn.ignore_`` bool False | Whether ignore done for target value | Enable it for some
| ``done`` | calculation. | fake termination env
15 ``collect.n_sample`` int [8, 128] | The number of training samples of a | It varies from
| call of collector. | different envs
16 | ``collect.unroll`` int 1 | unroll length of an iteration | In RNN, unroll_len>1
| ``_len``
== ==================== ======== ============== ======================================== =======================
"""
config = dict(
# (str) RL policy register name (refer to function "POLICY_REGISTRY").
type='r2d2',
# (bool) Whether to use cuda for network.
cuda=False,
# (bool) Whether the RL algorithm is on-policy or off-policy.
on_policy=False,
# (bool) Whether to use priority(priority sample, IS weight, update priority)
priority=True,
# (bool) Whether to use Importance Sampling Weight to correct biased update. If True, priority must be True.
priority_IS_weight=True,
# (float) Reward's future discount factor, aka. gamma.
discount_factor=0.997,
# (int) N-step reward for target q_value estimation
nstep=5,
# (int) the timestep of burnin operation, which is designed to RNN hidden state difference
# caused by off-policy
burnin_step=20,
# (int) the trajectory length to unroll the RNN network minus
# the timestep of burnin operation
learn_unroll_len=80,
# learn_mode config
learn=dict(
# (int) The number of training updates (iterations) to perform after each data collection by the collector.
# A larger "update_per_collect" value implies a more off-policy approach.
# The whole pipeline process follows this cycle: collect data -> update policy -> collect data -> ...
update_per_collect=1,
# (int) The number of samples in a training batch.
batch_size=64,
# (float) The step size of gradient descent, determining the rate of learning.
learning_rate=0.0001,
# (int) Frequence of target network update.
# target_update_freq=100,
target_update_theta=0.001,
# (bool) whether use value_rescale function for predicted value
value_rescale=True,
# (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.
# In each collect phase, we collect a total of <n_sample> sequence samples.
n_sample=32,
# (bool) It is important that set key traj_len_inf=True here,
# to make sure self._traj_len=INF in serial_sample_collector.py.
# In R2D2 policy, for each collect_env, we want to collect data of length self._traj_len=INF
# unless the episode enters the 'done' state.
traj_len_inf=True,
# (int) `env_num` is used in hidden state, should equal to that one in env config (e.g. collector_env_num).
# User should specify this value in user config. `None` is a placeholder.
env_num=None,
),
# eval_mode config
eval=dict(
# (int) `env_num` is used in hidden state, should equal to that one in env config (e.g. evaluator_env_num).
# User should specify this value in user config.
env_num=None,
),
other=dict(
# Epsilon greedy with decay.
eps=dict(
# (str) Type of decay. Supports either 'exp' (exponential) or 'linear'.
type='exp',
# (float) Initial value of epsilon at the start.
start=0.95,
# (float) Final value of epsilon after decay.
end=0.05,
# (int) The number of environment steps over which epsilon should decay.
decay=10000,
),
replay_buffer=dict(
# (int) Maximum size of replay buffer. Usually, larger buffer size is better.
replay_buffer_size=10000,
),
),
)
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 R2D2, its registered name is ``drqn`` and the import_names is \
``ding.model.template.q_learning``.
"""
return 'drqn', ['ding.model.template.q_learning']
def _init_learn(self) -> None:
"""
Overview:
Initialize the learn mode of policy, including some attributes and modules. For R2D2, it mainly contains \
optimizer, algorithm-specific arguments such as burnin_step, value_rescale and gamma, main and target \
model. Because of the use of RNN, all the models should be wrappered with ``hidden_state`` which needs to \
be initialized with proper size.
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
self._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate)
self._gamma = self._cfg.discount_factor
self._nstep = self._cfg.nstep
self._burnin_step = self._cfg.burnin_step
self._value_rescale = self._cfg.learn.value_rescale
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,
)
self._learn_model = model_wrap(
self._model,
wrapper_name='hidden_state',
state_num=self._cfg.learn.batch_size,
)
self._learn_model = model_wrap(self._learn_model, wrapper_name='argmax_sample')
self._learn_model.reset()
self._target_model.reset()
def _data_preprocess_learn(self, data: List[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
"""
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, torch.Tensor]`): The processed data, including at least \
['main_obs', 'target_obs', 'burnin_obs', 'action', 'reward', 'done', 'weight']
"""
# data preprocess
data = timestep_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)
burnin_step = self._burnin_step
# data['done'], data['weight'], data['value_gamma'] is used in def _forward_learn() to calculate
# the q_nstep_td_error, should be length of [self._sequence_len-self._burnin_step]
ignore_done = self._cfg.learn.ignore_done
if ignore_done:
data['done'] = [None for _ in range(self._sequence_len - burnin_step)]
else:
data['done'] = data['done'][burnin_step:].float() # for computation of online model self._learn_model
# NOTE that after the proprocessing of get_nstep_return_data() in _get_train_sample
# the data['done'] [t] is already the n-step done
# if the data don't include 'weight' or 'value_gamma' then fill in None in a list
# with length of [self._sequence_len-self._burnin_step],
# below is two different implementation ways
if 'value_gamma' not in data:
data['value_gamma'] = [None for _ in range(self._sequence_len - burnin_step)]
else:
data['value_gamma'] = data['value_gamma'][burnin_step:]
if 'weight' not in data or data['weight'] is None:
data['weight'] = [None for _ in range(self._sequence_len - burnin_step)]
else:
data['weight'] = data['weight'] * torch.ones_like(data['done'])
# every timestep in sequence has same weight, which is the _priority_IS_weight in PER
# cut the seq_len from burn_in step to (seq_len - nstep) step
data['action'] = data['action'][burnin_step:-self._nstep]
# cut the seq_len from burn_in step to (seq_len - nstep) step
data['reward'] = data['reward'][burnin_step:-self._nstep]
# the burnin_nstep_obs is used to calculate the init hidden state of rnn for the calculation of the q_value,
# target_q_value, and target_q_action
# these slicing are all done in the outermost layer, which is the seq_len dim
data['burnin_nstep_obs'] = data['obs'][:burnin_step + self._nstep]
# the main_obs is used to calculate the q_value, the [bs:-self._nstep] means using the data from
# [bs] timestep to [self._sequence_len-self._nstep] timestep
data['main_obs'] = data['obs'][burnin_step:-self._nstep]
# the target_obs is used to calculate the target_q_value
data['target_obs'] = data['obs'][burnin_step + self._nstep:]
return data
def _forward_learn(self, data: List[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 (trajectory for R2D2) from the replay buffer and then \
returns the output result, including various training information such as loss, q value, priority.
Arguments:
- data (:obj:`List[List[Dict[int, Any]]]`): The input data used for policy forward, including a batch of \
training samples. For each dict element, 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 time and \
batch dimension by the utility functions ``self._data_preprocess_learn``. \
For R2D2, each element in list is a trajectory with the length of ``unroll_len``, and the element in \
trajectory list is a dict containing at least the following keys: ``obs``, ``action``, ``prev_state``, \
``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 R2D2Policy: ``ding.policy.tests.test_r2d2``.
"""
# forward
data = self._data_preprocess_learn(data) # output datatype: Dict
self._learn_model.train()
self._target_model.train()
# use the hidden state in timestep=0
# note the reset method is performed at the hidden state wrapper, to reset self._state.
self._learn_model.reset(data_id=None, state=data['prev_state'][0])
self._target_model.reset(data_id=None, state=data['prev_state'][0])
if len(data['burnin_nstep_obs']) != 0:
with torch.no_grad():
inputs = {'obs': data['burnin_nstep_obs'], 'enable_fast_timestep': True}
burnin_output = self._learn_model.forward(
inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep]
) # keys include 'logit', 'hidden_state' 'saved_state', \
# 'action', for their specific dim, please refer to DRQN model
burnin_output_target = self._target_model.forward(
inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep]
)
self._learn_model.reset(data_id=None, state=burnin_output['saved_state'][0])
inputs = {'obs': data['main_obs'], 'enable_fast_timestep': True}
q_value = self._learn_model.forward(inputs)['logit']
self._learn_model.reset(data_id=None, state=burnin_output['saved_state'][1])
self._target_model.reset(data_id=None, state=burnin_output_target['saved_state'][1])
next_inputs = {'obs': data['target_obs'], 'enable_fast_timestep': True}
with torch.no_grad():
target_q_value = self._target_model.forward(next_inputs)['logit']
# argmax_action double_dqn
target_q_action = self._learn_model.forward(next_inputs)['action']
action, reward, done, weight = data['action'], data['reward'], data['done'], data['weight']
value_gamma = data['value_gamma']
# T, B, nstep -> T, nstep, B
reward = reward.permute(0, 2, 1).contiguous()
loss = []
td_error = []
for t in range(self._sequence_len - self._burnin_step - self._nstep):
# here t=0 means timestep <self._burnin_step> in the original sample sequence, we minus self._nstep
# because for the last <self._nstep> timestep in the sequence, we don't have their target obs
td_data = q_nstep_td_data(
q_value[t], target_q_value[t], action[t], target_q_action[t], reward[t], done[t], weight[t]
)
if self._value_rescale:
l, e = q_nstep_td_error_with_rescale(td_data, self._gamma, self._nstep, value_gamma=value_gamma[t])
loss.append(l)
td_error.append(e.abs())
else:
l, e = q_nstep_td_error(td_data, self._gamma, self._nstep, value_gamma=value_gamma[t])
loss.append(l)
# td will be a list of the length
# <self._sequence_len - self._burnin_step - self._nstep>
# and each value is a tensor of the size batch_size
td_error.append(e.abs())
loss = sum(loss) / (len(loss) + 1e-8)
# using the mixture of max and mean absolute n-step TD-errors as the priority of the sequence
td_error_per_sample = 0.9 * torch.max(
torch.stack(td_error), dim=0
)[0] + (1 - 0.9) * (torch.sum(torch.stack(td_error), dim=0) / (len(td_error) + 1e-8))
# torch.max(torch.stack(td_error), dim=0) will return tuple like thing, please refer to torch.max
# td_error shape list(<self._sequence_len-self._burnin_step-self._nstep>, B),
# for example, (75,64)
# torch.sum(torch.stack(td_error), dim=0) can also be replaced with sum(td_error)
# update
self._optimizer.zero_grad()
loss.backward()
self._optimizer.step()
# after update
self._target_model.update(self._learn_model.state_dict())
# the information for debug
batch_range = torch.arange(action[0].shape[0])
q_s_a_t0 = q_value[0][batch_range, action[0]]
target_q_s_a_t0 = target_q_value[0][batch_range, target_q_action[0]]
return {
'cur_lr': self._optimizer.defaults['lr'],
'total_loss': loss.item(),
'priority': td_error_per_sample.tolist(), # note abs operation has been performed above
# the first timestep in the sequence, may not be the start of episode
'q_s_taken-a_t0': q_s_a_t0.mean().item(),
'target_q_s_max-a_t0': target_q_s_a_t0.mean().item(),
'q_s_a-mean_t0': q_value[0].mean().item(),
}
def _reset_learn(self, data_id: Optional[List[int]] = None) -> None:
"""
Overview:
Reset some stateful variables for learn mode when necessary, such as the hidden state of RNN or the \
memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \
varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \
different trajectories in ``data_id`` will have different hidden state in RNN.
Arguments:
- data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \
(i.e. RNN hidden_state in R2D2) specified by ``data_id``.
"""
self._learn_model.reset(data_id=data_id)
def _state_dict_learn(self) -> Dict[str, Any]:
"""
Overview:
Return the state_dict of learn mode, usually including model, target_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:
"""
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:
"""
Overview:
Initialize the collect mode of policy, including related attributes and modules. For R2D2, it contains the \
collect_model to balance the exploration and exploitation with epsilon-greedy sample mechanism and \
maintain the hidden state of rnn. Besides, there are some initialization operations about other \
algorithm-specific arguments such as burnin_step, unroll_len and nstep.
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``.
.. tip::
Some variables need to initialize independently in different modes, such as gamma and nstep in R2D2. This \
design is for the convenience of parallel execution of different policy modes.
"""
self._nstep = self._cfg.nstep
self._burnin_step = self._cfg.burnin_step
self._gamma = self._cfg.discount_factor
self._sequence_len = self._cfg.learn_unroll_len + self._cfg.burnin_step
self._unroll_len = self._sequence_len
# for r2d2, this hidden_state wrapper is to add the 'prev hidden state' for each transition.
# Note that collect env forms a batch and the key is added for the batch simultaneously.
self._collect_model = model_wrap(
self._model, wrapper_name='hidden_state', state_num=self._cfg.collect.env_num, save_prev_state=True
)
self._collect_model = model_wrap(self._collect_model, wrapper_name='eps_greedy_sample')
self._collect_model.reset()
def _forward_collect(self, data: Dict[int, Any], eps: float) -> 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. Besides, this policy also needs ``eps`` argument for \
exploration, i.e., classic epsilon-greedy exploration strategy.
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.
- eps (:obj:`float`): The epsilon value for exploration.
Returns:
- output (:obj:`Dict[int, Any]`): The output data of policy forward, including at least the action and \
other necessary data (prev_state) 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.
.. note::
RNN's hidden states are maintained in the policy, so we don't need pass them into data but to reset the \
hidden states with ``_reset_collect`` method when episode ends. Besides, the previous hidden states are \
necessary for training, so we need to return them in ``_process_transition`` 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 R2D2Policy: ``ding.policy.tests.test_r2d2``.
"""
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():
# in collect phase, inference=True means that each time we only pass one timestep data,
# so the we can get the hidden state of rnn: <prev_state> at each timestep.
output = self._collect_model.forward(data, data_id=data_id, eps=eps, inference=True)
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:
"""
Overview:
Reset some stateful variables for eval mode when necessary, such as the hidden state of RNN or the \
memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \
varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \
different environments/episodes in evaluation in ``data_id`` will have different hidden state in RNN.
Arguments:
- data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \
(i.e., RNN hidden_state in R2D2) specified by ``data_id``.
"""
self._collect_model.reset(data_id=data_id)
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 R2D2, it contains obs, action, prev_state, reward, and done.
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 given the observation \
as input. For R2D2, it contains the action and the prev_state of RNN.
- 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,
'action': policy_output['action'],
'prev_state': policy_output['prev_state'],
'reward': timestep.reward,
'done': timestep.done,
}
return transition
def _get_train_sample(self, transitions: 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 directly. In R2D2, a train sample is processed transitions with unroll_len \
length. This method is usually used in collectors to execute necessary \
RL data preprocessing before training, which can help learner amortize revelant time consumption. \
In addition, you can also implement this method as an identity function and do the data processing \
in ``self._forward_learn`` method.
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 sample is a fixed-length \
trajectory, and each element in a sample is the similar format as input transitions, but may contain \
more data for training, such as nstep reward and value_gamma factor.
"""
transitions = get_nstep_return_data(transitions, self._nstep, gamma=self._gamma)
return get_train_sample(transitions, self._unroll_len)
def _init_eval(self) -> None:
"""
Overview:
Initialize the eval mode of policy, including related attributes and modules. For R2D2, it contains the \
eval model to greedily select action with argmax q_value mechanism and main the hidden state.
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``.
"""
self._eval_model = model_wrap(self._model, wrapper_name='hidden_state', state_num=self._cfg.eval.env_num)
self._eval_model = model_wrap(self._eval_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`` often use argmax sample method to get actions that \
q_value is the highest.
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::
RNN's hidden states are maintained in the policy, so we don't need pass them into data but to reset the \
hidden states with ``_reset_eval`` method when the episode ends.
.. 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 R2D2Policy: ``ding.policy.tests.test_r2d2``.
"""
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, inference=True)
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:
"""
Overview:
Reset some stateful variables for eval mode when necessary, such as the hidden state of RNN or the \
memory bank of some special algortihms. If ``data_id`` is None, it means to reset all the stateful \
varaibles. Otherwise, it will reset the stateful variables according to the ``data_id``. For example, \
different environments/episodes in evaluation in ``data_id`` will have different hidden state in RNN.
Arguments:
- data_id (:obj:`Optional[List[int]]`): The id of the data, which is used to reset the stateful variables \
(i.e., RNN hidden_state in R2D2) specified by ``data_id``.
"""
self._eval_model.reset(data_id=data_id)
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() + [
'total_loss', 'priority', 'q_s_taken-a_t0', 'target_q_s_max-a_t0', 'q_s_a-mean_t0'
]