85 lines
2.3 KiB
Plaintext
85 lines
2.3 KiB
Plaintext
{
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policy_net: {
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ego_encoder: {
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input_dim: 5, // number of state vars
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hidden_n: 0,
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hidden_dim: 5,
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output_dim: 5
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},
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deepsets: {
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input_dim: 6, // number of relative state vars for others
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phi: {
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hidden_n: 2,
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hidden_dim: 20,
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},
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latent_dim: 20,
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rho: {
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hidden_n: 2,
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hidden_dim: 10,
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},
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output_dim: 10
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},
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path_encoder: {
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input_dim: 40, // 2 * path length for (x,y) coordinates
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hidden_n: 0,
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hidden_dim: 20,
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output_dim: 10,
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},
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head: {
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input_dim: 0, // computed in policy constructor
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hidden_n: 3,
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hidden_dim: 50,
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output_dim: 1, // number of outputs e.g. number of actions, or just one
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final_activation: 'sigmoid',
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},
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},
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value_net: {
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ego_encoder: {
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input_dim: 5, // number of state vars
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hidden_n: 0,
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hidden_dim: 5,
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output_dim: 5
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},
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deepsets: {
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input_dim: 6, // number of relative state vars for others
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phi: {
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hidden_n: 2,
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hidden_dim: 20,
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},
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latent_dim: 20,
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rho: {
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hidden_n: 2,
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hidden_dim: 10,
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},
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output_dim: 10
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},
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path_encoder: {
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input_dim: 40, // 2 * path length for (x,y) coordinates
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hidden_n: 0,
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hidden_dim: 20,
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output_dim: 10,
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},
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action_dim: 1, // number of actions
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head: {
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input_dim: 0, // computed in policy constructor
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hidden_n: 3,
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hidden_dim: 50,
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output_dim: 1, // number of outputs e.g. number of actions, or just one
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final_activation: 'id',
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},
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},
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policy_optim: {
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optimizer: 'adam',
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lr: 1e-3,
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weight_decay: 0.1,
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},
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value_optim: {
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optimizer: 'adam',
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lr: 1e-3,
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weight_decay: 0.1,
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},
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train_epochs: 200,
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train_batch_size: 32,
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discount: 0.95,
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clip_grad_norm: 1.,
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} |