MLP - SLM Lab

Multi-Layer Perceptron
Code: slm_lab/agent/net/mlp.py​
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/mlp.py#L12-L58​
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.
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{
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    ...
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    "agent": [{
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      "net": {
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        "type": "MLPNet",
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        "shared": false,  // whether to shared networks for Actor-Critic
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        "hid_layers": [256, 128, 64],
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        "hid_layers_activation": "relu",
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        "out_layer_activation": null,  // output layer activation
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        "init_fn": "xavier_uniform_",  // weight initialization
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        "clip_grad_val": 1.0,  // clip gradient by norm
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        "loss_spec": {  // default loss function used for regression
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          "name": "MSELoss"
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        },
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        "optim_spec": {  // the optimizer and its arguments
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          "name": "Adam",
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          "lr": 0.02
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        },
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        ...
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      }
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    }],
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    ...
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}
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For more concrete examples of net spec specific to algorithms, refer to the existing spec files.

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Multi-Layer Perceptron

Source Documentation

Example Net Spec