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from typing import List, Dict, Any, Tuple, Union
from collections import namedtuple
import torch
from torch import nn
from copy import deepcopy
from ding.torch_utils import Adam, to_device
from ding.rl_utils import get_train_sample
from ding.utils import POLICY_REGISTRY, deep_merge_dicts
from ding.utils.data import default_collate, default_decollate
from ding.policy import Policy
from ding.model import model_wrap
from ding.policy.common_utils import default_preprocess_learn
from .utils import imagine, compute_target, compute_actor_loss, RewardEMA, tensorstats
@POLICY_REGISTRY.register('dreamer')
class DREAMERPolicy(Policy):
config = dict(
# (str) RL policy register name (refer to function "POLICY_REGISTRY").
type='dreamer',
# (bool) Whether to use cuda for network and loss computation.
cuda=False,
# (int) Number of training samples (randomly collected) in replay buffer when training starts.
random_collect_size=5000,
# (bool) Whether to need policy-specific data in preprocess transition.
transition_with_policy_data=False,
# (int)
imag_horizon=15,
learn=dict(
# (float) Lambda for TD-lambda return.
lambda_=0.95,
# (float) Max norm of gradients.
grad_clip=100,
learning_rate=3e-5,
batch_size=16,
batch_length=64,
imag_sample=True,
slow_value_target=True,
slow_target_update=1,
slow_target_fraction=0.02,
discount=0.997,
reward_EMA=True,
actor_entropy=3e-4,
actor_state_entropy=0.0,
value_decay=0.0,
),
)
def default_model(self) -> Tuple[str, List[str]]:
return 'dreamervac', ['ding.model.template.vac']
def _init_learn(self) -> None:
r"""
Overview:
Learn mode init method. Called by ``self.__init__``.
Init the optimizer, algorithm config, main and target models.
"""
# Algorithm config
self._lambda = self._cfg.learn.lambda_
self._grad_clip = self._cfg.learn.grad_clip
self._critic = self._model.critic
self._actor = self._model.actor
if self._cfg.learn.slow_value_target:
self._slow_value = deepcopy(self._critic)
self._updates = 0
# Optimizer
self._optimizer_value = Adam(
self._critic.parameters(),
lr=self._cfg.learn.learning_rate,
)
self._optimizer_actor = Adam(
self._actor.parameters(),
lr=self._cfg.learn.learning_rate,
)
self._learn_model = model_wrap(self._model, wrapper_name='base')
self._learn_model.reset()
self._forward_learn_cnt = 0
if self._cfg.learn.reward_EMA:
self.reward_ema = RewardEMA(device=self._device)
def _forward_learn(self, start: dict, world_model, envstep) -> Dict[str, Any]:
# log dict
log_vars = {}
self._learn_model.train()
self._update_slow_target()
self._actor.requires_grad_(requires_grad=True)
# start is dict of {stoch, deter, logit}
if self._cuda:
start = to_device(start, self._device)
# train self._actor
imag_feat, imag_state, imag_action = imagine(
self._cfg.learn, world_model, start, self._actor, self._cfg.imag_horizon
)
reward = world_model.heads["reward"](world_model.dynamics.get_feat(imag_state)).mode()
actor_ent = self._actor(imag_feat).entropy()
state_ent = world_model.dynamics.get_dist(imag_state).entropy()
# this target is not scaled
# slow is flag to indicate whether slow_target is used for lambda-return
target, weights, base = compute_target(
self._cfg.learn, world_model, self._critic, imag_feat, imag_state, reward, actor_ent, state_ent
)
actor_loss, mets = compute_actor_loss(
self._cfg.learn,
self._actor,
self.reward_ema,
imag_feat,
imag_state,
imag_action,
target,
actor_ent,
state_ent,
weights,
base,
)
log_vars.update(mets)
value_input = imag_feat
self._actor.requires_grad_(requires_grad=False)
self._critic.requires_grad_(requires_grad=True)
value = self._critic(value_input[:-1].detach())
# to do
# target = torch.stack(target, dim=1)
# (time, batch, 1), (time, batch, 1) -> (time, batch)
value_loss = -value.log_prob(target.detach())
slow_target = self._slow_value(value_input[:-1].detach())
if self._cfg.learn.slow_value_target:
value_loss = value_loss - value.log_prob(slow_target.mode().detach())
if self._cfg.learn.value_decay:
value_loss += self._cfg.learn.value_decay * value.mode()
# (time, batch, 1), (time, batch, 1) -> (1,)
value_loss = torch.mean(weights[:-1] * value_loss[:, :, None])
self._critic.requires_grad_(requires_grad=False)
log_vars.update(tensorstats(value.mode(), "value"))
log_vars.update(tensorstats(target, "target"))
log_vars.update(tensorstats(reward, "imag_reward"))
log_vars.update(tensorstats(imag_action, "imag_action"))
log_vars["actor_ent"] = torch.mean(actor_ent).detach().cpu().numpy().item()
# ====================
# actor-critic update
# ====================
self._model.requires_grad_(requires_grad=True)
world_model.requires_grad_(requires_grad=True)
loss_dict = {
'critic_loss': value_loss,
'actor_loss': actor_loss,
}
norm_dict = self._update(loss_dict)
self._model.requires_grad_(requires_grad=False)
world_model.requires_grad_(requires_grad=False)
# =============
# after update
# =============
self._forward_learn_cnt += 1
return {
**log_vars,
**norm_dict,
**loss_dict,
}
def _update(self, loss_dict):
# update actor
self._optimizer_actor.zero_grad()
loss_dict['actor_loss'].backward()
actor_norm = nn.utils.clip_grad_norm_(self._model.actor.parameters(), self._grad_clip)
self._optimizer_actor.step()
# update critic
self._optimizer_value.zero_grad()
loss_dict['critic_loss'].backward()
critic_norm = nn.utils.clip_grad_norm_(self._model.critic.parameters(), self._grad_clip)
self._optimizer_value.step()
return {'actor_grad_norm': actor_norm, 'critic_grad_norm': critic_norm}
def _update_slow_target(self):
if self._cfg.learn.slow_value_target:
if self._updates % self._cfg.learn.slow_target_update == 0:
mix = self._cfg.learn.slow_target_fraction
for s, d in zip(self._critic.parameters(), self._slow_value.parameters()):
d.data = mix * s.data + (1 - mix) * d.data
self._updates += 1
def _state_dict_learn(self) -> Dict[str, Any]:
ret = {
'model': self._learn_model.state_dict(),
'optimizer_value': self._optimizer_value.state_dict(),
'optimizer_actor': self._optimizer_actor.state_dict(),
}
return ret
def _load_state_dict_learn(self, state_dict: Dict[str, Any]) -> None:
self._learn_model.load_state_dict(state_dict['model'])
self._optimizer_value.load_state_dict(state_dict['optimizer_value'])
self._optimizer_actor.load_state_dict(state_dict['optimizer_actor'])
def _init_collect(self) -> None:
self._unroll_len = self._cfg.collect.unroll_len
self._collect_model = model_wrap(self._model, wrapper_name='base')
self._collect_model.reset()
def _forward_collect(self, data: dict, world_model, envstep, reset=None, state=None) -> dict:
data_id = list(data.keys())
data = default_collate(list(data.values()))
if self._cuda:
data = to_device(data, self._device)
self._collect_model.eval()
if state is None:
batch_size = len(data_id)
latent = world_model.dynamics.initial(batch_size) # {logit, stoch, deter}
action = torch.zeros((batch_size, self._cfg.collect.action_size)).to(self._device)
else:
#state = default_collate(list(state.values()))
latent = to_device(default_collate(list(zip(*state))[0]), self._device)
action = to_device(default_collate(list(zip(*state))[1]), self._device)
if len(action.shape) == 1:
action = action.unsqueeze(-1)
if reset.any():
mask = 1 - reset
for key in latent.keys():
for i in range(latent[key].shape[0]):
latent[key][i] *= mask[i]
for i in range(len(action)):
action[i] *= mask[i]
data = data - 0.5
embed = world_model.encoder(data)
latent, _ = world_model.dynamics.obs_step(latent, action, embed, self._cfg.collect.collect_dyn_sample)
feat = world_model.dynamics.get_feat(latent)
actor = self._actor(feat)
action = actor.sample()
logprob = actor.log_prob(action)
latent = {k: v.detach() for k, v in latent.items()}
action = action.detach()
state = (latent, action)
output = {"action": action, "logprob": logprob, "state": state}
if self._cuda:
output = to_device(output, 'cpu')
output = default_decollate(output)
return {i: d for i, d in zip(data_id, output)}
def _process_transition(self, obs: Any, model_output: dict, timestep: namedtuple) -> 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']
- 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.
"""
transition = {
'obs': obs,
'action': model_output['action'],
# TODO(zp) random_collect just have action
#'logprob': model_output['logprob'],
'reward': timestep.reward,
'discount': timestep.info['discount'],
'done': timestep.done,
}
return transition
def _get_train_sample(self, data: list) -> Union[None, List[Any]]:
return get_train_sample(data, self._unroll_len)
def _init_eval(self) -> None:
self._eval_model = model_wrap(self._model, wrapper_name='base')
self._eval_model.reset()
def _forward_eval(self, data: dict, world_model, reset=None, state=None) -> dict:
data_id = list(data.keys())
data = default_collate(list(data.values()))
if self._cuda:
data = to_device(data, self._device)
self._eval_model.eval()
if state is None:
batch_size = len(data_id)
latent = world_model.dynamics.initial(batch_size) # {logit, stoch, deter}
action = torch.zeros((batch_size, self._cfg.collect.action_size)).to(self._device)
else:
#state = default_collate(list(state.values()))
latent = to_device(default_collate(list(zip(*state))[0]), self._device)
action = to_device(default_collate(list(zip(*state))[1]), self._device)
if len(action.shape) == 1:
action = action.unsqueeze(-1)
if reset.any():
mask = 1 - reset
for key in latent.keys():
for i in range(latent[key].shape[0]):
latent[key][i] *= mask[i]
for i in range(len(action)):
action[i] *= mask[i]
data = data - 0.5
embed = world_model.encoder(data)
latent, _ = world_model.dynamics.obs_step(latent, action, embed, self._cfg.collect.collect_dyn_sample)
feat = world_model.dynamics.get_feat(latent)
actor = self._actor(feat)
action = actor.mode()
logprob = actor.log_prob(action)
latent = {k: v.detach() for k, v in latent.items()}
action = action.detach()
state = (latent, action)
output = {"action": action, "logprob": logprob, "state": state}
if self._cuda:
output = to_device(output, 'cpu')
output = default_decollate(output)
return {i: d for i, d in zip(data_id, output)}
def _monitor_vars_learn(self) -> List[str]:
r"""
Overview:
Return variables' name if variables are to used in monitor.
Returns:
- vars (:obj:`List[str]`): Variables' name list.
"""
return [
'normed_target_mean', 'normed_target_std', 'normed_target_min', 'normed_target_max', 'EMA_005', 'EMA_095',
'actor_entropy', 'actor_state_entropy', 'value_mean', 'value_std', 'value_min', 'value_max', 'target_mean',
'target_std', 'target_min', 'target_max', 'imag_reward_mean', 'imag_reward_std', 'imag_reward_min',
'imag_reward_max', 'imag_action_mean', 'imag_action_std', 'imag_action_min', 'imag_action_max', 'actor_ent',
'actor_loss', 'critic_loss', 'actor_grad_norm', 'critic_grad_norm'
]
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