Source code for ding.policy.ngu
from typing import List, Dict, Any, Tuple, Union, Optional
from collections import namedtuple
import torch
import copy
from ding.torch_utils import Adam, to_device
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.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
[docs]@POLICY_REGISTRY.register('ngu')
class NGUPolicy(Policy):
r"""
Overview:
Policy class of NGU. The corresponding paper is `never give up: learning directed exploration strategies`.
Config:
== ==================== ======== ============== ======================================== =======================
ID Symbol Type Default Value Description Other(Shape)
== ==================== ======== ============== ======================================== =======================
1 ``type`` str dqn | 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='ngu',
# (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 use priority(priority sample, IS weight, update priority)
priority=True,
# (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.
priority_IS_weight=True,
# ==============================================================
# The following configs are algorithm-specific
# ==============================================================
# (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) <learn_unroll_len> is the total length of [sequence sample] minus
# the length of burnin part in [sequence sample],
# i.e., <sequence sample length> = <unroll_len> = <burnin_step> + <learn_unroll_len>
learn_unroll_len=80, # set this key according to the episode length
learn=dict(
update_per_collect=1,
batch_size=64,
learning_rate=0.0001,
# ==============================================================
# The following configs are algorithm-specific
# ==============================================================
# (float type) target_update_theta: Used for soft update of the target network,
# aka. Interpolation factor in polyak averaging for target networks.
target_update_theta=0.001,
# (bool) whether use value_rescale function for predicted value
value_rescale=True,
ignore_done=False,
),
collect=dict(
# NOTE: It is important that set key traj_len_inf=True here,
# to make sure self._traj_len=INF in serial_sample_collector.py.
# In sequence-based policy, for each collect_env,
# we want to collect data of length self._traj_len=INF
# unless the episode enters the 'done' state.
# In each collect phase, we collect a total of <n_sample> sequence samples.
n_sample=32,
traj_len_inf=True,
# `env_num` is used in hidden state, should equal to that one in env config.
# User should specify this value in user config.
env_num=None,
),
eval=dict(
# `env_num` is used in hidden state, should equal to that one in env config.
# User should specify this value in user config.
env_num=None,
),
other=dict(
eps=dict(
type='exp',
start=0.95,
end=0.05,
decay=10000,
),
replay_buffer=dict(replay_buffer_size=10000, ),
),
)
def default_model(self) -> Tuple[str, List[str]]:
return 'ngu', ['ding.model.template.ngu']
def _init_learn(self) -> None:
r"""
Overview:
Init the learner model of R2D2Policy
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
- nstep (:obj:`int`): The num of n step return
- value_rescale (:obj:`bool`): Whether to use value rescaled loss in algorithm
- burnin_step (:obj:`int`): The num of step of burnin
"""
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)
# here we should not adopt the 'assign' mode of target network here because the reset bug
# self._target_model = model_wrap(
# self._target_model,
# wrapper_name='target',
# update_type='assign',
# update_kwargs={'freq': self._cfg.learn.target_update_freq}
# )
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, save_prev_state=True
)
self._learn_model = model_wrap(
self._model, wrapper_name='hidden_state', state_num=self._cfg.learn.batch_size, save_prev_state=True
)
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:
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, including at least \
['main_obs', 'target_obs', 'burnin_obs', 'action', 'reward', 'done', 'weight']
- data_info (:obj:`dict`): the data info, such as replay_buffer_idx, replay_unique_id
"""
# 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)
bs = 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 - bs - self._nstep)]
else:
data['done'] = data['done'][bs:].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 - bs)]
else:
data['value_gamma'] = data['value_gamma'][bs:]
if 'weight' not in data:
data['weight'] = [None for _ in range(self._sequence_len - bs)]
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
# 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
data['burnin_nstep_obs'] = data['obs'][:bs + self._nstep]
data['burnin_nstep_action'] = data['action'][:bs + self._nstep]
data['burnin_nstep_reward'] = data['reward'][:bs + self._nstep]
data['burnin_nstep_beta'] = data['beta'][:bs + self._nstep]
# split obs into three parts 'burnin_obs' [0:bs], 'main_obs' [bs:bs+nstep], 'target_obs' [bs+nstep:]
# data['burnin_obs'] = data['obs'][:bs]
data['main_obs'] = data['obs'][bs:-self._nstep]
data['target_obs'] = data['obs'][bs + self._nstep:]
# data['burnin_action'] = data['action'][:bs]
data['main_action'] = data['action'][bs:-self._nstep]
data['target_action'] = data['action'][bs + self._nstep:]
# data['burnin_reward'] = data['reward'][:bs]
data['main_reward'] = data['reward'][bs:-self._nstep]
data['target_reward'] = data['reward'][bs + self._nstep:]
# data['burnin_beta'] = data['beta'][:bs]
data['main_beta'] = data['beta'][bs:-self._nstep]
data['target_beta'] = data['beta'][bs + self._nstep:]
# Note that Must be here after the previous slicing operation
data['action'] = data['action'][bs:-self._nstep]
data['reward'] = data['reward'][bs:-self._nstep]
return data
def _forward_learn(self, data: dict) -> Dict[str, Any]:
r"""
Overview:
Forward and backward function of learn mode.
Acquire the data, calculate the loss and optimize learner model.
Arguments:
- data (:obj:`dict`): Dict type data, including at least \
['main_obs', 'target_obs', 'burnin_obs', 'action', 'reward', 'done', 'weight']
Returns:
- info_dict (:obj:`Dict[str, Any]`): Including cur_lr and total_loss
- cur_lr (:obj:`float`): Current learning rate
- total_loss (:obj:`float`): The calculated loss
"""
# forward
data = self._data_preprocess_learn(data)
self._learn_model.train()
self._target_model.train()
# use the hidden state in timestep=0
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'],
'action': data['burnin_nstep_action'],
'reward': data['burnin_nstep_reward'],
'beta': data['burnin_nstep_beta'],
'enable_fast_timestep': True
}
tmp = self._learn_model.forward(
inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep]
)
tmp_target = self._target_model.forward(
inputs, saved_state_timesteps=[self._burnin_step, self._burnin_step + self._nstep]
)
inputs = {
'obs': data['main_obs'],
'action': data['main_action'],
'reward': data['main_reward'],
'beta': data['main_beta'],
'enable_fast_timestep': True
}
self._learn_model.reset(data_id=None, state=tmp['saved_state'][0])
q_value = self._learn_model.forward(inputs)['logit']
self._learn_model.reset(data_id=None, state=tmp['saved_state'][1])
self._target_model.reset(data_id=None, state=tmp_target['saved_state'][1])
next_inputs = {
'obs': data['target_obs'],
'action': data['target_action'],
'reward': data['target_reward'],
'beta': data['target_beta'],
'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 = [
None for _ in range(self._sequence_len - self._burnin_step)
] # NOTE this is important, because we use diffrent gamma according to their beta in NGU alg.
# T, B, nstep -> T, nstep, B
reward = reward.permute(0, 2, 1).contiguous()
loss = []
td_error = []
self._gamma = [self.index_to_gamma[int(i)] for i in data['main_beta'][0]] # T, B -> B, e.g. 75,64 -> 64
# reward torch.Size([4, 5, 64])
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_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))
# 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.abs().tolist(),
# 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:
self._learn_model.reset(data_id=data_id)
def _state_dict_learn(self) -> Dict[str, Any]:
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:
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.
"""
assert 'unroll_len' not in self._cfg.collect, "ngu use default <unroll_len = learn_unroll_len + burnin_step>"
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
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()
self.index_to_gamma = { # NOTE
i: 1 - torch.exp(
(
(self._cfg.collect.env_num - 1 - i) * torch.log(torch.tensor(1 - 0.997)) +
i * torch.log(torch.tensor(1 - 0.99))
) / (self._cfg.collect.env_num - 1)
)
for i in range(self._cfg.collect.env_num)
}
# NOTE: for NGU policy collect phase
self.beta_index = {
i: torch.randint(0, self._cfg.collect.env_num, [1])
for i in range(self._cfg.collect.env_num)
}
# epsilon=0.4, alpha=9
self.eps = {i: 0.4 ** (1 + 8 * i / (self._cfg.collect.env_num - 1)) for i in range(self._cfg.collect.env_num)}
def _forward_collect(self, data: dict) -> dict:
r"""
Overview:
Collect output according to eps_greedy plugin
Arguments:
- data (:obj:`dict`): Dict type data, including at least ['obs'].
Returns:
- data (:obj:`dict`): The collected data
"""
data_id = list(data.keys())
data = default_collate(list(data.values()))
obs = data['obs']
prev_action = data['prev_action'].long()
prev_reward_extrinsic = data['prev_reward_extrinsic']
beta_index = default_collate(list(self.beta_index.values()))
if len(data_id) != self._cfg.collect.env_num:
# in case, some env is in reset state and only return part data
beta_index = beta_index[data_id]
if self._cuda:
obs = to_device(obs, self._device)
beta_index = to_device(beta_index, self._device)
prev_action = to_device(prev_action, self._device)
prev_reward_extrinsic = to_device(prev_reward_extrinsic, self._device)
# TODO(pu): add prev_reward_intrinsic to network input,
# reward uses some kind of embedding instead of 1D value
data = {
'obs': obs,
'prev_action': prev_action,
'prev_reward_extrinsic': prev_reward_extrinsic,
'beta': beta_index
}
self._collect_model.eval()
with torch.no_grad():
output = self._collect_model.forward(data, data_id=data_id, eps=self.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:
self._collect_model.reset(data_id=data_id)
# NOTE: for NGU policy, in collect phase, each episode, we sample a new beta for each env
if data_id is not None:
self.beta_index[data_id[0]] = torch.randint(0, self._cfg.collect.env_num, [1])
def _process_transition(self, obs: Any, model_output: dict, timestep: namedtuple, env_id) -> 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']
- timestep (:obj:`namedtuple`): Output after env step, including at least ['reward', 'done'] \
(here 'obs' indicates obs after env step).
Returns:
- transition (:obj:`dict`): Dict type transition data.
"""
if hasattr(timestep, 'null'):
transition = {
'beta': self.beta_index[env_id],
'obs': obs['obs'], # NOTE: input obs including obs, prev_action, prev_reward_extrinsic
'action': model_output['action'],
'prev_state': model_output['prev_state'],
'reward': timestep.reward,
'done': timestep.done,
'null': timestep.null,
}
else:
transition = {
'beta': self.beta_index[env_id],
'obs': obs['obs'], # NOTE: input obs including obs, prev_action, prev_reward_extrinsic
'action': model_output['action'],
'prev_state': model_output['prev_state'],
'reward': timestep.reward,
'done': timestep.done,
'null': False,
}
return transition
def _get_train_sample(self, data: list) -> Union[None, List[Any]]:
r"""
Overview:
Get the trajectory and the n step return data, then sample from the n_step return data
Arguments:
- data (:obj:`list`): The trajectory's cache
Returns:
- samples (:obj:`dict`): The training samples generated
"""
data = get_nstep_return_data(data, self._nstep, gamma=self.index_to_gamma[int(data[0]['beta'])].item())
return get_train_sample(data, self._sequence_len)
def _init_eval(self) -> None:
r"""
Overview:
Evaluate mode init method. Called by ``self.__init__``.
Init eval model with argmax strategy.
"""
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()
# NOTE: for NGU policy eval phase
# beta_index = 0 -> beta is approximately 0
self.beta_index = {i: torch.tensor([0]) for i in range(self._cfg.eval.env_num)}
def _forward_eval(self, data: dict) -> dict:
r"""
Overview:
Forward function of collect mode, similar to ``self._forward_collect``.
Arguments:
- data (:obj:`dict`): Dict type data, including at least ['obs'].
Returns:
- output (:obj:`dict`): Dict type data, including at least inferred action according to input obs.
"""
data_id = list(data.keys())
data = default_collate(list(data.values()))
obs = data['obs']
prev_action = data['prev_action'].long()
prev_reward_extrinsic = data['prev_reward_extrinsic']
beta_index = default_collate(list(self.beta_index.values()))
if len(data_id) != self._cfg.collect.env_num:
# in case, some env is in reset state and only return part data
beta_index = beta_index[data_id]
if self._cuda:
obs = to_device(obs, self._device)
beta_index = to_device(beta_index, self._device)
prev_action = to_device(prev_action, self._device)
prev_reward_extrinsic = to_device(prev_reward_extrinsic, self._device)
# TODO(pu): add prev_reward_intrinsic to network input,
# reward uses some kind of embedding instead of 1D value
data = {
'obs': obs,
'prev_action': prev_action,
'prev_reward_extrinsic': prev_reward_extrinsic,
'beta': beta_index
}
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:
self._eval_model.reset(data_id=data_id)
def _monitor_vars_learn(self) -> List[str]:
return super()._monitor_vars_learn() + [
'total_loss', 'priority', 'q_s_taken-a_t0', 'target_q_s_max-a_t0', 'q_s_a-mean_t0'
]