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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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22
experiments/experiment.py
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@@ -90,6 +90,9 @@ def get_full_config(ray_config:dict, method:str)->dict:
if method == 'bc':
from src.bc import bc_config
config = bc_config(ray_config)
elif method == 'vd':
from src.value_dice import vd_config
config = vd_config(ray_config)
else:
raise NotImplementedError
return config
@@ -118,6 +121,25 @@ def get_ray_config(method:str)->dict:
"head_hidden_dim": tune.lograndint(16,257),
"head_final_activation": tune.choice(['sigmoid', None]),
}
elif method == 'vd':
ray_config = {
"policy_lr": tune.loguniform(1e-5, 1e-3),
"value_lr": tune.loguniform(1e-5, 1e-3),
"policy_weight_decay": tune.choice([0, 0.1]),
"value_weight_decay": tune.choice([0, 0.1]),
"train_batch_size": tune.choice([16,32,64]),
"deepsets_phi_hidden_n": tune.randint(1,5),
"deepsets_phi_hidden_dim": tune.lograndint(8,65),
"deepsets_latent_dim": tune.lograndint(8,129),
"deepsets_rho_hidden_n": tune.randint(0,3),
"deepsets_rho_hidden_dim": tune.lograndint(8,129),
"deepsets_output_dim": tune.lograndint(4,129),
"head_hidden_n": tune.randint(1,6),
"head_hidden_dim": tune.lograndint(16,257),
"head_final_activation": tune.choice(['sigmoid', None]),
"clip_grad_norm": tune.choice([.5, 1., 5., 10.]),
"discount": tune.choice([.95, .99])
}
else:
raise NotImplementedError
return ray_config

83
src/value_dice/value_dice.py
View File

@@ -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