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Add value dice ray config

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This commit is contained in:
Johannes Fischer
2021-08-05 18:33:34 +02:00
parent 8bce5d15f6
commit bf4c19a4d0
2 changed files with 80 additions and 25 deletions
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83
src/value_dice/value_dice.py
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@@ -11,39 +11,72 @@ from src.util.nn_training import optimizer_factory
from tqdm import tqdm
import json5
from ray import tune
def vd_config(ray_config):
config = {
'ego_encoder': {'input_dim': 5, 'hidden_n': 0, 'hidden_dim':0, 'output_dim': 0},
'deepsets': {
'input_dim': 6,
'phi': {
'hidden_n': ray_config['deepsets_phi_hidden_n'],
'hidden_dim': ray_config['deepsets_phi_hidden_dim']
},
'latent_dim': ray_config['deepsets_latent_dim'],
'rho': {
'hidden_n': ray_config['deepsets_rho_hidden_n'],
'hidden_dim': ray_config['deepsets_rho_hidden_dim']
},
'output_dim': ray_config['deepsets_output_dim']
'policy_net': {
'ego_encoder': {'input_dim': 5, 'hidden_n': 0, 'hidden_dim':0, 'output_dim': 0},
'deepsets': {
'input_dim': 6,
'phi': {
'hidden_n': ray_config['deepsets_phi_hidden_n'],
'hidden_dim': ray_config['deepsets_phi_hidden_dim']
},
'latent_dim': ray_config['deepsets_latent_dim'],
'rho': {
'hidden_n': ray_config['deepsets_rho_hidden_n'],
'hidden_dim': ray_config['deepsets_rho_hidden_dim']
},
'output_dim': ray_config['deepsets_output_dim']
},
'path_encoder': {'input_dim': 40, 'hidden_n': 0, 'hidden_dim': 0, 'output_dim': 0},
'head': {
'input_dim': 0, # computed in constructor
'hidden_n': ray_config['head_hidden_n'],
'hidden_dim': ray_config['head_hidden_dim'],
'output_dim': 1, # number of outputs e.g. number of actions, or just one
'final_activation': ray_config['head_final_activation'],
},
},
'path_encoder': {'input_dim': 40, 'hidden_n': 0, 'hidden_dim': 0, 'output_dim': 0},
'head': {
'input_dim': 0, # computed in constructor
'hidden_n': ray_config['head_hidden_n'],
'hidden_dim': ray_config['head_hidden_dim'],
'output_dim': 1, # number of outputs e.g. number of actions, or just one
'final_activation': ray_config['head_final_activation'],
'value_net': {
'ego_encoder': {'input_dim': 5, 'hidden_n': 0, 'hidden_dim':0, 'output_dim': 0},
'deepsets': {
'input_dim': 6,
'phi': {
'hidden_n': ray_config['deepsets_phi_hidden_n'],
'hidden_dim': ray_config['deepsets_phi_hidden_dim']
},
'latent_dim': ray_config['deepsets_latent_dim'],
'rho': {
'hidden_n': ray_config['deepsets_rho_hidden_n'],
'hidden_dim': ray_config['deepsets_rho_hidden_dim']
},
'output_dim': ray_config['deepsets_output_dim']
},
'path_encoder': {'input_dim': 40, 'hidden_n': 0, 'hidden_dim': 0, 'output_dim': 0},
'action_dim': 1,
'head': {
'input_dim': 0, # computed in constructor
'hidden_n': ray_config['head_hidden_n'],
'hidden_dim': ray_config['head_hidden_dim'],
'output_dim': 1, # number of outputs e.g. number of actions, or just one
'final_activation': ray_config['head_final_activation'],
},
},
'optim': {
'policy_optim': {
'optimizer':'adam',
'lr':ray_config['lr'],
'weight_decay':ray_config['weight_decay']
'lr':ray_config['policy_lr'],
'weight_decay':ray_config['policy_weight_decay']
},
'value_optim': {
'optimizer':'adam',
'lr':ray_config['value_lr'],
'weight_decay':ray_config['value_weight_decay']
},
'train_epochs': 40,
'train_batch_size': ray_config['train_batch_size'],
'loss': ray_config['loss'],
'discount': ray_config['discount'],
'clip_grad_norm': ray_config['clip_grad_norm'],
}
return config