feature(nyz): add PPOF new interface support

ding/bonus/__init__.py

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+from .ppof import PPOF

ding/bonus/config.py

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+from easydict import EasyDict
+import gym
+from ding.envs import BaseEnv, DingEnvWrapper
+from ding.policy import PPOFPolicy
+
+
+def get_instance_config(env: str) -> EasyDict:
+    cfg = PPOFPolicy.default_config()
+    if env == 'lunarlander_discrete':
+        cfg.n_sample = 400
+    elif env == 'lunarlander_continuous':
+        cfg.action_space = 'continuous'
+        cfg.n_sample = 400
+    elif env == 'rocket_landing':
+        cfg.n_sample = 2048
+        cfg.adv_norm = False
+        cfg.model = dict(
+            encoder_hidden_size_list=[64, 64, 128],
+            actor_head_hidden_size=128,
+            critic_head_hidden_size=128,
+        )
+    elif env == 'drone_fly':
+        cfg.action_space = 'continuous'
+        cfg.adv_norm = False
+        cfg.epoch_per_collect = 5
+        cfg.learning_rate = 5e-5
+        cfg.n_sample = 640
+    elif env == 'hybrid_moving':
+        cfg.action_space = 'hybrid'
+        cfg.n_sample = 3200
+        cfg.entropy_weight = 0.03
+        cfg.batch_size = 320
+        cfg.adv_norm = False
+        cfg.model = dict(
+            encoder_hidden_size_list=[256, 128, 64, 64],
+            sigma_type='fixed',
+            fixed_sigma_value=0.3,
+            bound_type='tanh',
+        )
+    else:
+        raise KeyError("not supported env type: {}".format(env))
+    return cfg
+
+
+def get_instance_env(env: str) -> BaseEnv:
+    if env == 'lunarlander_discrete':
+        return DingEnvWrapper(gym.make('LunarLander-v2'))
+    elif env == 'lunarlander_continuous':
+        return DingEnvWrapper(gym.make('LunarLander-v2', continuous=True))
+    elif env == 'rocket_landing':
+        from dizoo.rocket.envs import RocketEnv
+        cfg = EasyDict({
+            'task': 'landing',
+            'max_steps': 800,
+        })
+        return RocketEnv(cfg)
+    elif env == 'drone_fly':
+        from dizoo.gym_pybullet_drones.envs import GymPybulletDronesEnv
+        cfg = EasyDict({
+            'env_id': 'flythrugate-aviary-v0',
+            'action_type': 'VEL',
+        })
+        return GymPybulletDronesEnv(cfg)
+    elif env == 'hybrid_moving':
+        import gym_hybrid
+        return DingEnvWrapper(gym.make('Moving-v0'))
+    else:
+        raise KeyError("not supported env type: {}".format(env))
+
+
+def get_hybrid_shape(action_space) -> EasyDict:
+    return EasyDict({
+        'action_type_shape': action_space[0].n,
+        'action_args_shape': action_space[1].shape,
+    })

ding/bonus/model.py

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+from typing import Union, Optional
+from easydict import EasyDict
+import torch
+import torch.nn as nn
+import treetensor.torch as ttorch
+from copy import deepcopy
+from ding.utils import SequenceType, squeeze
+from ding.model.common import ReparameterizationHead, RegressionHead, MultiHead, \
+    FCEncoder, ConvEncoder, IMPALAConvEncoder
+from ding.torch_utils import MLP, fc_block
+
+
+class DiscretePolicyHead(nn.Module):
+
+    def __init__(
+            self,
+            hidden_size: int,
+            output_size: int,
+            layer_num: int = 1,
+            activation: Optional[nn.Module] = nn.ReLU(),
+            norm_type: Optional[str] = None,
+    ) -> None:
+        super(DiscretePolicyHead, self).__init__()
+        self.main = nn.Sequential(
+            MLP(
+                hidden_size,
+                hidden_size,
+                hidden_size,
+                layer_num,
+                layer_fn=nn.Linear,
+                activation=activation,
+                norm_type=norm_type
+            ), fc_block(hidden_size, output_size)
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.main(x)
+
+
+class PPOFModel(nn.Module):
+    mode = ['compute_actor', 'compute_critic', 'compute_actor_critic']
+
+    def __init__(
+        self,
+        obs_shape: Union[int, SequenceType],
+        action_shape: Union[int, SequenceType, EasyDict],
+        action_space: str = 'discrete',
+        share_encoder: bool = True,
+        encoder_hidden_size_list: SequenceType = [128, 128, 64],
+        actor_head_hidden_size: int = 64,
+        actor_head_layer_num: int = 1,
+        critic_head_hidden_size: int = 64,
+        critic_head_layer_num: int = 1,
+        activation: Optional[nn.Module] = nn.ReLU(),
+        norm_type: Optional[str] = None,
+        sigma_type: Optional[str] = 'independent',
+        fixed_sigma_value: Optional[int] = 0.3,
+        bound_type: Optional[str] = None,
+        encoder: Optional[torch.nn.Module] = None,
+    ) -> None:
+        super(PPOFModel, self).__init__()
+        obs_shape = squeeze(obs_shape)
+        action_shape = squeeze(action_shape)
+        self.obs_shape, self.action_shape = obs_shape, action_shape
+        self.share_encoder = share_encoder
+
+        # Encoder Type
+        def new_encoder(outsize):
+            if isinstance(obs_shape, int) or len(obs_shape) == 1:
+                return FCEncoder(
+                    obs_shape=obs_shape,
+                    hidden_size_list=encoder_hidden_size_list,
+                    activation=activation,
+                    norm_type=norm_type
+                )
+            elif len(obs_shape) == 3:
+                return ConvEncoder(
+                    obs_shape=obs_shape,
+                    hidden_size_list=encoder_hidden_size_list,
+                    activation=activation,
+                    norm_type=norm_type
+                )
+            else:
+                raise RuntimeError(
+                    "not support obs_shape for pre-defined encoder: {}, please customize your own encoder".
+                    format(obs_shape)
+                )
+
+        if self.share_encoder:
+            assert actor_head_hidden_size == critic_head_hidden_size, \
+                "actor and critic network head should have same size."
+            if encoder:
+                if isinstance(encoder, torch.nn.Module):
+                    self.encoder = encoder
+                else:
+                    raise ValueError("illegal encoder instance.")
+            else:
+                self.encoder = new_encoder(actor_head_hidden_size)
+        else:
+            if encoder:
+                if isinstance(encoder, torch.nn.Module):
+                    self.actor_encoder = encoder
+                    self.critic_encoder = deepcopy(encoder)
+                else:
+                    raise ValueError("illegal encoder instance.")
+            else:
+                self.actor_encoder = new_encoder(actor_head_hidden_size)
+                self.critic_encoder = new_encoder(critic_head_hidden_size)
+
+        # Head Type
+        self.critic_head = RegressionHead(
+            critic_head_hidden_size, 1, critic_head_layer_num, activation=activation, norm_type=norm_type
+        )
+        self.action_space = action_space
+        assert self.action_space in ['discrete', 'continuous', 'hybrid'], self.action_space
+        if self.action_space == 'continuous':
+            self.multi_head = False
+            self.actor_head = ReparameterizationHead(
+                actor_head_hidden_size,
+                action_shape,
+                actor_head_layer_num,
+                sigma_type=sigma_type,
+                activation=activation,
+                norm_type=norm_type,
+                bound_type=bound_type
+            )
+        elif self.action_space == 'discrete':
+            actor_head_cls = DiscretePolicyHead
+            multi_head = not isinstance(action_shape, int)
+            self.multi_head = multi_head
+            if multi_head:
+                self.actor_head = MultiHead(
+                    actor_head_cls,
+                    actor_head_hidden_size,
+                    action_shape,
+                    layer_num=actor_head_layer_num,
+                    activation=activation,
+                    norm_type=norm_type
+                )
+            else:
+                self.actor_head = actor_head_cls(
+                    actor_head_hidden_size,
+                    action_shape,
+                    actor_head_layer_num,
+                    activation=activation,
+                    norm_type=norm_type
+                )
+        elif self.action_space == 'hybrid':  # HPPO
+            # hybrid action space: action_type(discrete) + action_args(continuous),
+            # such as {'action_type_shape': torch.LongTensor([0]), 'action_args_shape': torch.FloatTensor([0.1, -0.27])}
+            action_shape.action_args_shape = squeeze(action_shape.action_args_shape)
+            action_shape.action_type_shape = squeeze(action_shape.action_type_shape)
+            actor_action_args = ReparameterizationHead(
+                actor_head_hidden_size,
+                action_shape.action_args_shape,
+                actor_head_layer_num,
+                sigma_type=sigma_type,
+                fixed_sigma_value=fixed_sigma_value,
+                activation=activation,
+                norm_type=norm_type,
+                bound_type=bound_type,
+            )
+            actor_action_type = DiscretePolicyHead(
+                actor_head_hidden_size,
+                action_shape.action_type_shape,
+                actor_head_layer_num,
+                activation=activation,
+                norm_type=norm_type,
+            )
+            self.actor_head = nn.ModuleList([actor_action_type, actor_action_args])
+
+        # must use list, not nn.ModuleList
+        if self.share_encoder:
+            self.actor = [self.encoder, self.actor_head]
+            self.critic = [self.encoder, self.critic_head]
+        else:
+            self.actor = [self.actor_encoder, self.actor_head]
+            self.critic = [self.critic_encoder, self.critic_head]
+        # Convenient for calling some apis (e.g. self.critic.parameters()),
+        # but may cause misunderstanding when `print(self)`
+        self.actor = nn.ModuleList(self.actor)
+        self.critic = nn.ModuleList(self.critic)
+
+    def forward(self, inputs: ttorch.Tensor, mode: str) -> ttorch.Tensor:
+        assert mode in self.mode, "not support forward mode: {}/{}".format(mode, self.mode)
+        return getattr(self, mode)(inputs)
+
+    def compute_actor(self, x: ttorch.Tensor) -> ttorch.Tensor:
+        if self.share_encoder:
+            x = self.encoder(x)
+        else:
+            x = self.actor_encoder(x)
+
+        if self.action_space == 'discrete':
+            return self.actor_head(x)
+        elif self.action_space == 'continuous':
+            x = self.actor_head(x)  # mu, sigma
+            return ttorch.as_tensor(x)
+        elif self.action_space == 'hybrid':
+            action_type = self.actor_head[0](x)
+            action_args = self.actor_head[1](x)
+            return ttorch.as_tensor({'action_type': action_type, 'action_args': action_args})
+
+    def compute_critic(self, x: ttorch.Tensor) -> ttorch.Tensor:
+        if self.share_encoder:
+            x = self.encoder(x)
+        else:
+            x = self.critic_encoder(x)
+        x = self.critic_head(x)
+        return x['pred']
+
+    def compute_actor_critic(self, x: ttorch.Tensor) -> ttorch.Tensor:
+        if self.share_encoder:
+            actor_embedding = critic_embedding = self.encoder(x)
+        else:
+            actor_embedding = self.actor_encoder(x)
+            critic_embedding = self.critic_encoder(x)
+
+        value = self.critic_head(critic_embedding)['pred']
+
+        if self.action_space == 'discrete':
+            logit = self.actor_head(actor_embedding)
+            return ttorch.as_tensor({'logit': logit, 'value': value})
+        elif self.action_space == 'continuous':
+            x = self.actor_head(actor_embedding)
+            return ttorch.as_tensor({'logit': x, 'value': value})
+        elif self.action_space == 'hybrid':
+            action_type = self.actor_head[0](actor_embedding)
+            action_args = self.actor_head[1](actor_embedding)
+            return ttorch.as_tensor({'logit': {'action_type': action_type, 'action_args': action_args}, 'value': value})