Source code for ding.model.template.bcq
from typing import Union, Dict, Optional, List
from easydict import EasyDict
import numpy as np
import torch
import torch.nn as nn
from ding.utils import SequenceType, squeeze, MODEL_REGISTRY
from ..common import RegressionHead, ReparameterizationHead
from .vae import VanillaVAE
[docs]@MODEL_REGISTRY.register('bcq')
class BCQ(nn.Module):
"""
Overview:
Model of BCQ (Batch-Constrained deep Q-learning).
Off-Policy Deep Reinforcement Learning without Exploration.
https://arxiv.org/abs/1812.02900
Interface:
``forward``, ``compute_actor``, ``compute_critic``, ``compute_vae``, ``compute_eval``
Property:
``mode``
"""
mode = ['compute_actor', 'compute_critic', 'compute_vae', 'compute_eval']
[docs] def __init__(
self,
obs_shape: Union[int, SequenceType],
action_shape: Union[int, SequenceType, EasyDict],
actor_head_hidden_size: List = [400, 300],
critic_head_hidden_size: List = [400, 300],
activation: Optional[nn.Module] = nn.ReLU(),
vae_hidden_dims: List = [750, 750],
phi: float = 0.05
) -> None:
"""
Overview:
Initialize neural network, i.e. agent Q network and actor.
Arguments:
- obs_shape (:obj:`int`): the dimension of observation state
- action_shape (:obj:`int`): the dimension of action shape
- actor_hidden_size (:obj:`list`): the list of hidden size of actor
- critic_hidden_size (:obj:'list'): the list of hidden size of critic
- activation (:obj:`nn.Module`): Activation function in network, defaults to nn.ReLU().
- vae_hidden_dims (:obj:`list`): the list of hidden size of vae
"""
super(BCQ, self).__init__()
obs_shape: int = squeeze(obs_shape)
action_shape = squeeze(action_shape)
self.action_shape = action_shape
self.input_size = obs_shape
self.phi = phi
critic_input_size = self.input_size + action_shape
self.critic = nn.ModuleList()
for _ in range(2):
net = []
d = critic_input_size
for dim in critic_head_hidden_size:
net.append(nn.Linear(d, dim))
net.append(activation)
d = dim
net.append(nn.Linear(d, 1))
self.critic.append(nn.Sequential(*net))
net = []
d = critic_input_size
for dim in actor_head_hidden_size:
net.append(nn.Linear(d, dim))
net.append(activation)
d = dim
net.append(nn.Linear(d, 1))
self.actor = nn.Sequential(*net)
self.vae = VanillaVAE(action_shape, obs_shape, action_shape * 2, vae_hidden_dims)
[docs] def forward(self, inputs: Dict[str, torch.Tensor], mode: str) -> Dict[str, torch.Tensor]:
"""
Overview:
The unique execution (forward) method of BCQ method, and one can indicate different modes to implement \
different computation graph, including ``compute_actor`` and ``compute_critic`` in BCQ.
Mode compute_actor:
Arguments:
- inputs (:obj:`Dict`): Input dict data, including obs and action tensor.
Returns:
- output (:obj:`Dict`): Output dict data, including action tensor.
Mode compute_critic:
Arguments:
- inputs (:obj:`Dict`): Input dict data, including obs and action tensor.
Returns:
- output (:obj:`Dict`): Output dict data, including q_value tensor.
Mode compute_vae:
Arguments:
- inputs (:obj:`Dict`): Input dict data, including obs and action tensor.
Returns:
- outputs (:obj:`Dict`): Dict containing keywords ``recons_action`` \
(:obj:`torch.Tensor`), ``prediction_residual`` (:obj:`torch.Tensor`), \
``input`` (:obj:`torch.Tensor`), ``mu`` (:obj:`torch.Tensor`), \
``log_var`` (:obj:`torch.Tensor`) and ``z`` (:obj:`torch.Tensor`).
Mode compute_eval:
Arguments:
- inputs (:obj:`Dict`): Input dict data, including obs and action tensor.
Returns:
- output (:obj:`Dict`): Output dict data, including action tensor.
Examples:
>>> inputs = {'obs': torch.randn(4, 32), 'action': torch.randn(4, 6)}
>>> model = BCQ(32, 6)
>>> outputs = model(inputs, mode='compute_actor')
>>> outputs = model(inputs, mode='compute_critic')
>>> outputs = model(inputs, mode='compute_vae')
>>> outputs = model(inputs, mode='compute_eval')
.. note::
For specific examples, one can refer to API doc of ``compute_actor`` and ``compute_critic`` respectively.
"""
assert mode in self.mode, "not support forward mode: {}/{}".format(mode, self.mode)
return getattr(self, mode)(inputs)
[docs] def compute_critic(self, inputs: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Overview:
Use critic network to compute q value.
Arguments:
- inputs (:obj:`Dict`): Input dict data, including obs and action tensor.
Returns:
- outputs (:obj:`Dict`): Dict containing keywords ``q_value`` (:obj:`torch.Tensor`).
Shapes:
- inputs (:obj:`Dict`): :math:`(B, N, D)`, where B is batch size, N is sample number, D is input dimension.
- outputs (:obj:`Dict`): :math:`(B, N)`.
Examples:
>>> inputs = {'obs': torch.randn(4, 32), 'action': torch.randn(4, 6)}
>>> model = BCQ(32, 6)
>>> outputs = model.compute_critic(inputs)
"""
obs, action = inputs['obs'], inputs['action']
if len(action.shape) == 1: # (B, ) -> (B, 1)
action = action.unsqueeze(1)
x = torch.cat([obs, action], dim=-1)
x = [m(x).squeeze() for m in self.critic]
return {'q_value': x}
[docs] def compute_actor(self, inputs: Dict[str, torch.Tensor]) -> Dict[str, Union[torch.Tensor, Dict[str, torch.Tensor]]]:
"""
Overview:
Use actor network to compute action.
Arguments:
- inputs (:obj:`Dict`): Input dict data, including obs and action tensor.
Returns:
- outputs (:obj:`Dict`): Dict containing keywords ``action`` (:obj:`torch.Tensor`).
Shapes:
- inputs (:obj:`Dict`): :math:`(B, N, D)`, where B is batch size, N is sample number, D is input dimension.
- outputs (:obj:`Dict`): :math:`(B, N)`.
Examples:
>>> inputs = {'obs': torch.randn(4, 32), 'action': torch.randn(4, 6)}
>>> model = BCQ(32, 6)
>>> outputs = model.compute_actor(inputs)
"""
input = torch.cat([inputs['obs'], inputs['action']], -1)
x = self.actor(input)
action = self.phi * 1 * torch.tanh(x)
action = (action + inputs['action']).clamp(-1, 1)
return {'action': action}
[docs] def compute_vae(self, inputs: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Overview:
Use vae network to compute action.
Arguments:
- inputs (:obj:`Dict`): Input dict data, including obs and action tensor.
Returns:
- outputs (:obj:`Dict`): Dict containing keywords ``recons_action`` (:obj:`torch.Tensor`), \
``prediction_residual`` (:obj:`torch.Tensor`), ``input`` (:obj:`torch.Tensor`), \
``mu`` (:obj:`torch.Tensor`), ``log_var`` (:obj:`torch.Tensor`) and ``z`` (:obj:`torch.Tensor`).
Shapes:
- inputs (:obj:`Dict`): :math:`(B, N, D)`, where B is batch size, N is sample number, D is input dimension.
- outputs (:obj:`Dict`): :math:`(B, N)`.
Examples:
>>> inputs = {'obs': torch.randn(4, 32), 'action': torch.randn(4, 6)}
>>> model = BCQ(32, 6)
>>> outputs = model.compute_vae(inputs)
"""
return self.vae.forward(inputs)
[docs] def compute_eval(self, inputs: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Overview:
Use actor network to compute action.
Arguments:
- inputs (:obj:`Dict`): Input dict data, including obs and action tensor.
Returns:
- outputs (:obj:`Dict`): Dict containing keywords ``action`` (:obj:`torch.Tensor`).
Shapes:
- inputs (:obj:`Dict`): :math:`(B, N, D)`, where B is batch size, N is sample number, D is input dimension.
- outputs (:obj:`Dict`): :math:`(B, N)`.
Examples:
>>> inputs = {'obs': torch.randn(4, 32), 'action': torch.randn(4, 6)}
>>> model = BCQ(32, 6)
>>> outputs = model.compute_eval(inputs)
"""
obs = inputs['obs']
obs_rep = obs.clone().unsqueeze(0).repeat_interleave(100, dim=0)
z = torch.randn((obs_rep.shape[0], obs_rep.shape[1], self.action_shape * 2)).to(obs.device).clamp(-0.5, 0.5)
sample_action = self.vae.decode_with_obs(z, obs_rep)['reconstruction_action']
action = self.compute_actor({'obs': obs_rep, 'action': sample_action})['action']
q = self.compute_critic({'obs': obs_rep, 'action': action})['q_value'][0]
idx = q.argmax(dim=0).unsqueeze(0).unsqueeze(-1)
idx = idx.repeat_interleave(action.shape[-1], dim=-1)
action = action.gather(0, idx).squeeze()
return {'action': action}