DdpgCnnQNet

class torchrl.modules.DdpgCnnQNet(conv_net_kwargs: dict | None = None, mlp_net_kwargs: dict | None = None, use_avg_pooling: bool = True, device: DEVICE_TYPING | None = None)

DDPG Convolutional Q-value class.

Presented in “CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING”, https://arxiv.org/pdf/1509.02971.pdf

The DDPG Q-value network takes as input an observation and an action, and returns a scalar from it.

Parameters:

• conv_net_kwargs (dict, optional) –

  kwargs for the convolutional network. Defaults to

  >>> {
  ...     'in_features': None,
  ...     "num_cells": [32, 64, 128],
  ...     "kernel_sizes": [8, 4, 3],
  ...     "strides": [4, 2, 1],
  ...     "paddings": [0, 0, 1],
  ...     'activation_class': nn.ELU,
  ...     'norm_class': None,
  ...     'aggregator_class': nn.AdaptiveAvgPool2d,
  ...     'aggregator_kwargs': {},
  ...     'squeeze_output': True,
  ... }
  

• mlp_net_kwargs (dict, optional) –

  kwargs for MLP. Defaults to

  >>> {
  ...     'in_features': None,
  ...     'out_features': 1,
  ...     'depth': 2,
  ...     'num_cells': 200,
  ...     'activation_class': nn.ELU,
  ...     'bias_last_layer': True,
  ... }
  

• use_avg_pooling (bool, optional) – if True, a AvgPooling layer is used to aggregate the output. Default is True.

• device (torch.device, optional) – device to create the module on.

Examples

>>> from torchrl.modules import DdpgCnnQNet
>>> import torch
>>> net = DdpgCnnQNet()
>>> print(net)
DdpgCnnQNet(
  (convnet): ConvNet(
    (0): LazyConv2d(0, 32, kernel_size=(8, 8), stride=(4, 4))
    (1): ELU(alpha=1.0)
    (2): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2))
    (3): ELU(alpha=1.0)
    (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (5): ELU(alpha=1.0)
    (6): AdaptiveAvgPool2d(output_size=(1, 1))
    (7): Squeeze2dLayer()
  )
  (mlp): MLP(
    (0): LazyLinear(in_features=0, out_features=200, bias=True)
    (1): ELU(alpha=1.0)
    (2): Linear(in_features=200, out_features=200, bias=True)
    (3): ELU(alpha=1.0)
    (4): Linear(in_features=200, out_features=1, bias=True)
  )
)
>>> obs = torch.zeros(1, 3, 64, 64)
>>> action = torch.zeros(1, 4)
>>> value = net(obs, action)
>>> print(value.shape)
torch.Size([1, 1])

forward(observation: Tensor, action: Tensor) → Tensor

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.