DdpgCnnQNet¶
- class torchrl.modules.DdpgCnnQNet(conv_net_kwargs: Optional[dict] = None, mlp_net_kwargs: Optional[dict] = None, use_avg_pooling: bool = True, device: Optional[Union[device, str, int]] = None)[source]¶
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
, aAvgPooling
layer is used to aggregate the output. Default isTrue
.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 [source]¶
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.