Multi-Layer Perceptron

Code: slm_lab/agent/net/

These networks take a single state as input. They are composed of a sequence of dense (fully connected) layers. MLPs are general purpose, simple networks. Well suited for environments with a low dimensional state space, or a state space with no spatial structure.

Source Documentation

Refer to the class documentation and example net spec from the source: slm_lab/agent/net/

Example Net Spec

This specification instantiates an MLP with 3 hidden layers of 256, 128, and 64 nodes respectively, rectified linear (ReLU) activations, and the Adam optimizer with a learning rate of 0.02. The rest of the spec is annotated below.

    "agent": [{
      "net": {
        "type": "MLPNet",
        "shared": false,  // whether to shared networks for Actor-Critic
        "hid_layers": [256, 128, 64],
        "hid_layers_activation": "relu",
        "out_layer_activation": null,  // output layer activation
        "init_fn": "xavier_uniform_",  // weight initialization
        "clip_grad_val": 1.0,  // clip gradient by norm
        "loss_spec": {  // default loss function used for regression
          "name": "MSELoss"
        "optim_spec": {  // the optimizer and its arguments
          "name": "Adam",
          "lr": 0.02

For more concrete examples of net spec specific to algorithms, refer to the existing spec files.

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