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c51.py
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c51.py
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from typing import List, Dict, Any, Tuple, Union
import copy
import torch
from ding.torch_utils import Adam, to_device
from ding.rl_utils import dist_nstep_td_data, dist_nstep_td_error, get_train_sample, get_nstep_return_data
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY
from ding.utils.data import default_collate, default_decollate
from .dqn import DQNPolicy
from .common_utils import default_preprocess_learn
@POLICY_REGISTRY.register('c51')
class C51Policy(DQNPolicy):
r"""
Overview:
Policy class of C51 algorithm.
Config:
== ==================== ======== ============== ======================================== =======================
ID Symbol Type Default Value Description Other(Shape)
== ==================== ======== ============== ======================================== =======================
1 ``type`` str c51 | 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 ``model.v_min`` float -10 | Value of the smallest atom
| in the support set.
6 ``model.v_max`` float 10 | Value of the largest atom
| in the support set.
7 ``model.n_atom`` int 51 | Number of atoms in the support set
| of the value distribution.
8 | ``other.eps`` float 0.95 | Start value for epsilon decay.
| ``.start`` |
9 | ``other.eps`` float 0.1 | End value for epsilon decay.
| ``.end``
10 | ``discount_`` float 0.97, | Reward's future discount factor, aka. | may be 1 when sparse
| ``factor`` [0.95, 0.999] | gamma | reward env
11 ``nstep`` int 1, | N-step reward discount sum for target
| q_value estimation
12 | ``learn.update`` int 3 | 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='c51',
# (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=False,
# (float) Reward's future discount factor, aka. gamma.
discount_factor=0.97,
# (int) N-step reward for target q_value estimation
nstep=1,
model=dict(
v_min=-10,
v_max=10,
n_atom=51,
),
learn=dict(
# How many updates(iterations) to train after collector's one collection.
# Bigger "update_per_collect" means bigger off-policy.
# collect data -> update policy-> collect data -> ...
update_per_collect=3,
batch_size=64,
learning_rate=0.001,
# ==============================================================
# The following configs are algorithm-specific
# ==============================================================
# (int) Frequence of target network update.
target_update_freq=100,
# (bool) Whether ignore done(usually for max step termination env)
ignore_done=False,
),
# collect_mode config
collect=dict(
# (int) Only one of [n_sample, n_step, n_episode] shoule be set
# n_sample=8,
# (int) Cut trajectories into pieces with length "unroll_len".
unroll_len=1,
),
eval=dict(),
# other config
other=dict(
# Epsilon greedy with decay.
eps=dict(
# (str) Decay type. Support ['exp', 'linear'].
type='exp',
start=0.95,
end=0.1,
# (int) Decay length(env step)
decay=10000,
),
replay_buffer=dict(replay_buffer_size=10000, )
),
)
def default_model(self) -> Tuple[str, List[str]]:
return 'c51dqn', ['ding.model.template.q_learning']
def _init_learn(self) -> None:
r"""
Overview:
Learn mode init method. Called by ``self.__init__``.
Init the optimizer, algorithm config, main and target models.
"""
self._priority = self._cfg.priority
# Optimizer
self._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate)
self._gamma = self._cfg.discount_factor
self._nstep = self._cfg.nstep
self._v_max = self._cfg.model.v_max
self._v_min = self._cfg.model.v_min
self._n_atom = self._cfg.model.n_atom
# use wrapper instead of plugin
self._target_model = copy.deepcopy(self._model)
self._target_model = model_wrap(
self._target_model,
wrapper_name='target',
update_type='assign',
update_kwargs={'freq': self._cfg.learn.target_update_freq}
)
self._learn_model = model_wrap(self._model, wrapper_name='argmax_sample')
self._learn_model.reset()
self._target_model.reset()
def _forward_learn(self, data: dict) -> Dict[str, Any]:
r"""
Overview:
Forward and backward function of learn mode.
Arguments:
- data (:obj:`dict`): Dict type data, including at least ['obs', 'action', 'reward', 'next_obs']
Returns:
- info_dict (:obj:`Dict[str, Any]`): Including current lr and loss.
"""
data = default_preprocess_learn(
data, use_priority=self._priority, ignore_done=self._cfg.learn.ignore_done, use_nstep=True
)
if self._cuda:
data = to_device(data, self._device)
# ====================
# Q-learning forward
# ====================
self._learn_model.train()
self._target_model.train()
# Current q value (main model)
output = self._learn_model.forward(data['obs'])
q_value = output['logit']
q_value_dist = output['distribution']
# Target q value
with torch.no_grad():
target_output = self._target_model.forward(data['next_obs'])
target_q_value_dist = target_output['distribution']
target_q_value = target_output['logit']
# Max q value action (main model)
target_q_action = self._learn_model.forward(data['next_obs'])['action']
data_n = dist_nstep_td_data(
q_value_dist, target_q_value_dist, data['action'], target_q_action, data['reward'], data['done'],
data['weight']
)
value_gamma = data.get('value_gamma')
loss, td_error_per_sample = dist_nstep_td_error(
data_n, self._gamma, self._v_min, self._v_max, self._n_atom, nstep=self._nstep, value_gamma=value_gamma
)
# ====================
# Q-learning update
# ====================
self._optimizer.zero_grad()
loss.backward()
if self._cfg.multi_gpu:
self.sync_gradients(self._learn_model)
self._optimizer.step()
# =============
# after update
# =============
self._target_model.update(self._learn_model.state_dict())
return {
'cur_lr': self._optimizer.defaults['lr'],
'total_loss': loss.item(),
'q_value': q_value.mean().item(),
'target_q_value': target_q_value.mean().item(),
'priority': td_error_per_sample.abs().tolist(),
# Only discrete action satisfying len(data['action'])==1 can return this and draw histogram on tensorboard.
# '[histogram]action_distribution': data['action'],
}
def _monitor_vars_learn(self) -> List[str]:
return ['cur_lr', 'total_loss', 'q_value', 'target_q_value']
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:
"""
Overview:
Collect mode init method. Called by ``self.__init__``. Initialize necessary arguments for nstep return \
calculation and collect_model for exploration (eps_greedy_sample).
"""
self._unroll_len = self._cfg.collect.unroll_len
self._gamma = self._cfg.discount_factor # necessary for parallel
self._nstep = self._cfg.nstep # necessary for parallel
self._collect_model = model_wrap(self._model, wrapper_name='eps_greedy_sample')
self._collect_model.reset()
def _forward_collect(self, data: Dict[int, Any], eps: float) -> Dict[int, Any]:
"""
Overview:
Forward computation graph of collect mode(collect training data), with eps_greedy for exploration.
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.
- eps (:obj:`float`): epsilon value for exploration, which is decayed by collected env step.
Returns:
- output (:obj:`Dict[int, Any]`): The dict of predicting policy_output(action) for the interaction with \
env and the constructing of transition.
ArgumentsKeys:
- necessary: ``obs``
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, eps=eps)
if self._cuda:
output = to_device(output, 'cpu')
output = default_decollate(output)
return {i: d for i, d in zip(data_id, output)}
def _get_train_sample(self, data: list) -> Union[None, List[Any]]:
"""
Overview:
Calculate nstep return data and transform a trajectory into many train samples.
Arguments:
- data (:obj:`list`): The collected data of a trajectory, which is a list that contains dict elements.
Returns:
- samples (:obj:`dict`): The training samples generated.
"""
data = get_nstep_return_data(data, self._nstep, gamma=self._gamma)
return get_train_sample(data, self._unroll_len)