File size: 13,695 Bytes
079c32c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
from typing import List, Dict, Any, Tuple, Union
from collections import namedtuple
import torch
import copy
import numpy as np
from torch.distributions import Independent, Normal

from ding.torch_utils import Adam, to_device
from ding.rl_utils import ppo_data, ppo_error, ppo_policy_error, ppo_policy_data, get_gae_with_default_last_value, \
    v_nstep_td_data, v_nstep_td_error, get_nstep_return_data, get_train_sample, gae, gae_data, ppo_error_continuous,\
    get_gae
from ding.model import model_wrap
from ding.utils import POLICY_REGISTRY, split_data_generator, RunningMeanStd
from ding.utils.data import default_collate, default_decollate
from .base_policy import Policy
from .common_utils import default_preprocess_learn


@POLICY_REGISTRY.register('offppo_collect_traj')
class OffPPOCollectTrajPolicy(Policy):
    r"""
    Overview:
        Policy class of off policy PPO algorithm to collect expert traj for R2D3.
    """
    config = dict(
        # (str) RL policy register name (refer to function "POLICY_REGISTRY").
        type='ppo',
        # (bool) Whether to use cuda for network.
        cuda=False,
        # (bool) Whether the RL algorithm is on-policy or off-policy. (Note: in practice PPO can be off-policy used)
        on_policy=True,
        # (bool) Whether to use priority(priority sample, IS weight, update priority)
        priority=False,
        # (bool) Whether use Importance Sampling Weight to correct biased update. If True, priority must be True.
        priority_IS_weight=False,
        # (bool) Whether to use nstep_return for value loss
        nstep_return=False,
        nstep=3,
        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=5,
            batch_size=64,
            learning_rate=0.001,
            # ==============================================================
            # The following configs is algorithm-specific
            # ==============================================================
            # (float) The loss weight of value network, policy network weight is set to 1
            value_weight=0.5,
            # (float) The loss weight of entropy regularization, policy network weight is set to 1
            entropy_weight=0.01,
            # (float) PPO clip ratio, defaults to 0.2
            clip_ratio=0.2,
            # (bool) Whether to use advantage norm in a whole training batch
            adv_norm=False,
            ignore_done=False,
        ),
        collect=dict(
            # ==============================================================
            # The following configs is algorithm-specific
            # ==============================================================
            # (float) Reward's future discount factor, aka. gamma.
            discount_factor=0.99,
            # (float) GAE lambda factor for the balance of bias and variance(1-step td and mc)
            gae_lambda=0.95,
        ),
        eval=dict(),
        other=dict(replay_buffer=dict(replay_buffer_size=10000, ), ),
    )

    def default_model(self) -> Tuple[str, List[str]]:
        return 'vac', ['ding.model.template.vac']

    def _init_learn(self) -> None:
        r"""
        Overview:
            Learn mode init method. Called by ``self.__init__``.
            Init the optimizer, algorithm config and the main model.
        """
        self._priority = self._cfg.priority
        self._priority_IS_weight = self._cfg.priority_IS_weight
        assert not self._priority and not self._priority_IS_weight, "Priority is not implemented in PPO"
        # Orthogonal init
        for m in self._model.modules():
            if isinstance(m, torch.nn.Conv2d):
                torch.nn.init.orthogonal_(m.weight)
            if isinstance(m, torch.nn.Linear):
                torch.nn.init.orthogonal_(m.weight)
        # Optimizer
        self._optimizer = Adam(self._model.parameters(), lr=self._cfg.learn.learning_rate)
        self._learn_model = model_wrap(self._model, wrapper_name='base')

        # Algorithm config
        self._value_weight = self._cfg.learn.value_weight
        self._entropy_weight = self._cfg.learn.entropy_weight
        self._clip_ratio = self._cfg.learn.clip_ratio
        self._adv_norm = self._cfg.learn.adv_norm
        self._nstep = self._cfg.nstep
        self._nstep_return = self._cfg.nstep_return
        # Main model
        self._learn_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
        Returns:
            - info_dict (:obj:`Dict[str, Any]`):
              Including current lr, total_loss, policy_loss, value_loss, entropy_loss, \
                        adv_abs_max, approx_kl, clipfrac
        """
        data = default_preprocess_learn(data, ignore_done=self._cfg.learn.ignore_done, use_nstep=self._nstep_return)
        if self._cuda:
            data = to_device(data, self._device)
        # ====================
        # PPO forward
        # ====================

        self._learn_model.train()
        # normal ppo
        if not self._nstep_return:
            output = self._learn_model.forward(data['obs'], mode='compute_actor_critic')
            adv = data['adv']
            return_ = data['value'] + adv
            if self._adv_norm:
                # Normalize advantage in a total train_batch
                adv = (adv - adv.mean()) / (adv.std() + 1e-8)
            # Calculate ppo error
            ppodata = ppo_data(
                output['logit'], data['logit'], data['action'], output['value'], data['value'], adv, return_,
                data['weight']
            )
            ppo_loss, ppo_info = ppo_error(ppodata, self._clip_ratio)
            wv, we = self._value_weight, self._entropy_weight
            total_loss = ppo_loss.policy_loss + wv * ppo_loss.value_loss - we * ppo_loss.entropy_loss

        else:
            output = self._learn_model.forward(data['obs'], mode='compute_actor')
            adv = data['adv']
            if self._adv_norm:
                # Normalize advantage in a total train_batch
                adv = (adv - adv.mean()) / (adv.std() + 1e-8)

            # Calculate ppo error
            ppodata = ppo_policy_data(output['logit'], data['logit'], data['action'], adv, data['weight'])
            ppo_policy_loss, ppo_info = ppo_policy_error(ppodata, self._clip_ratio)
            wv, we = self._value_weight, self._entropy_weight
            next_obs = data.get('next_obs')
            value_gamma = data.get('value_gamma')
            reward = data.get('reward')
            # current value
            value = self._learn_model.forward(data['obs'], mode='compute_critic')
            # target value
            next_data = {'obs': next_obs}
            target_value = self._learn_model.forward(next_data['obs'], mode='compute_critic')
            # TODO what should we do here to keep shape
            assert self._nstep > 1
            td_data = v_nstep_td_data(
                value['value'], target_value['value'], reward.t(), data['done'], data['weight'], value_gamma
            )
            # calculate v_nstep_td critic_loss
            critic_loss, td_error_per_sample = v_nstep_td_error(td_data, self._gamma, self._nstep)
            ppo_loss_data = namedtuple('ppo_loss', ['policy_loss', 'value_loss', 'entropy_loss'])
            ppo_loss = ppo_loss_data(ppo_policy_loss.policy_loss, critic_loss, ppo_policy_loss.entropy_loss)
            total_loss = ppo_policy_loss.policy_loss + wv * critic_loss - we * ppo_policy_loss.entropy_loss

        # ====================
        # PPO 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': ppo_loss.policy_loss.item(),
            'value_loss': ppo_loss.value_loss.item(),
            'entropy_loss': ppo_loss.entropy_loss.item(),
            'adv_abs_max': adv.abs().max().item(),
            'approx_kl': ppo_info.approx_kl,
            'clipfrac': ppo_info.clipfrac,
        }

    def _state_dict_learn(self) -> Dict[str, Any]:
        return {
            'model': self._learn_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._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.
        """
        self._unroll_len = self._cfg.collect.unroll_len
        # self._collect_model = model_wrap(self._model, wrapper_name='multinomial_sample')
        # NOTE this policy is to collect expert traj, so we have to use argmax_sample wrapper
        self._collect_model = model_wrap(self._model, wrapper_name='argmax_sample')
        self._collect_model.reset()
        self._gamma = self._cfg.collect.discount_factor
        self._gae_lambda = self._cfg.collect.gae_lambda
        self._nstep = self._cfg.nstep
        self._nstep_return = self._cfg.nstep_return

    def _forward_collect(self, data: dict) -> dict:
        r"""
        Overview:
            Forward function for collect mode
        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()))
        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_critic')
        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:
        """
        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'],
            # 'prev_state': model_output['prev_state'],
            'prev_state': None,
            'reward': timestep.reward,
            'done': timestep.done,
        }
        return transition

    def _get_train_sample(self, data: list) -> Union[None, List[Any]]:
        r"""
        Overview:
            Get the trajectory and calculate GAE, return one data to cache for next time calculation
        Arguments:
            - data (:obj:`list`): The trajectory's cache
        Returns:
            - samples (:obj:`dict`): The training samples generated
        """
        from copy import deepcopy
        # data_one_step = deepcopy(get_nstep_return_data(data, 1, gamma=self._gamma))
        data_one_step = deepcopy(data)
        data = get_nstep_return_data(data, self._nstep, gamma=self._gamma)
        for i in range(len(data)):
            # here we record the one-step done, we don't need record one-step reward,
            # because the n-step reward in data already include one-step reward
            data[i]['done_one_step'] = data_one_step[i]['done']
        return get_train_sample(data, self._unroll_len)  # self._unroll_len_add_burnin_step

    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='argmax_sample')
        self._eval_model.reset()

    def _forward_eval(self, data: dict) -> dict:
        r"""
        Overview:
            Forward function for eval 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()))
        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)
        return {i: d for i, d in zip(data_id, output)}

    def _monitor_vars_learn(self) -> List[str]:
        return super()._monitor_vars_learn() + [
            'policy_loss', 'value_loss', 'entropy_loss', 'adv_abs_max', 'approx_kl', 'clipfrac'
        ]