release
This commit is contained in:
allenzren
@@ -0,0 +1,70 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
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||||
run:
|
||||
dir: ${logdir}
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||||
_target_: agent.pretrain.train_diffusion_agent.TrainDiffusionAgent
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||||
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||||
name: avoid_m1_pre_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
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||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m1/train.pkl
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||||
|
||||
seed: 42
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||||
device: cuda:0
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||||
env: avoid
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||||
mode: d56_r12 # M1, desired modes 5 and 6, required modes 1 and 2
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||||
obs_dim: 4
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||||
action_dim: 2
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||||
transition_dim: ${action_dim}
|
||||
denoising_steps: 20
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||||
horizon_steps: 4
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||||
cond_steps: 1
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||||
|
||||
wandb:
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entity: ${oc.env:DPPO_WANDB_ENTITY}
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||||
project: d3il-${env}-pretrain
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||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
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||||
n_epochs: 15000
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||||
batch_size: 16
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 15000
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||||
warmup_steps: 100
|
||||
min_lr: 1e-5
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||||
epoch_start_ema: 20
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||||
update_ema_freq: 10
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||||
save_model_freq: 1000
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||||
|
||||
model:
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||||
_target_: model.diffusion.diffusion.DiffusionModel
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||||
predict_epsilon: True
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||||
denoised_clip_value: 1.0
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||||
network:
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||||
_target_: model.diffusion.mlp_diffusion.DiffusionMLP
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||||
time_dim: 16
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||||
mlp_dims: [512, 512, 512]
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||||
activation_type: ReLU
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||||
residual_style: True
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||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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||||
horizon_steps: ${horizon_steps}
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||||
transition_dim: ${transition_dim}
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||||
horizon_steps: ${horizon_steps}
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||||
obs_dim: ${obs_dim}
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||||
action_dim: ${action_dim}
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||||
transition_dim: ${transition_dim}
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||||
denoising_steps: ${denoising_steps}
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||||
cond_steps: ${cond_steps}
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||||
device: ${device}
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||||
|
||||
ema:
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||||
decay: 0.995
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||||
|
||||
train_dataset:
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_target_: agent.dataset.sequence.StitchedActionSequenceDataset
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||||
dataset_path: ${train_dataset_path}
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||||
horizon_steps: ${horizon_steps}
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||||
cond_steps: ${cond_steps}
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||||
device: ${device}
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||||
@@ -0,0 +1,62 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
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||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
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||||
|
||||
name: avoid_m1_pre_gaussian_mlp_ta${horizon_steps}
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||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m1/train.pkl
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||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
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||||
mode: d56_r12 # M1, desired modes 5 and 6, required modes 1 and 2
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obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
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||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 10000
|
||||
batch_size: 16
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
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||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
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||||
update_ema_freq: 10
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||||
save_model_freq: 1000
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||||
|
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model:
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_target_: model.common.gaussian.GaussianModel
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||||
network:
|
||||
_target_: model.common.mlp_gaussian.Gaussian_MLP
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||||
mlp_dims: [256, 256, 256] # smaller MLP for less overfitting
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activation_type: ReLU
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||||
residual_style: True
|
||||
fixed_std: 0.1
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
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||||
|
||||
ema:
|
||||
decay: 0.995
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||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
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||||
dataset_path: ${train_dataset_path}
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||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
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||||
device: ${device}
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||||
@@ -0,0 +1,64 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: avoid_m1_pre_gmm_mlp_ta${horizon_steps}
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||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m1/train.pkl
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||||
|
||||
seed: 42
|
||||
device: cuda:0
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||||
env: avoid
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||||
mode: d56_r12 # M1, desired modes 5 and 6, required modes 1 and 2
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||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
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||||
num_modes: 5
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||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
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||||
project: d3il-${env}-pretrain
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||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 10000
|
||||
batch_size: 32
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
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||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gmm.GMMModel
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||||
network:
|
||||
_target_: model.common.mlp_gmm.GMM_MLP
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||||
mlp_dims: [256, 256] # smaller MLP for less overfitting
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||||
activation_type: ReLU
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||||
residual_style: False
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||||
fixed_std: 0.1
|
||||
num_modes: ${num_modes}
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||||
cond_dim: ${obs_dim}
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||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
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||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
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||||
dataset_path: ${train_dataset_path}
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||||
horizon_steps: ${horizon_steps}
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||||
cond_steps: ${cond_steps}
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||||
device: ${device}
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||||
@@ -0,0 +1,70 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
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||||
_target_: agent.pretrain.train_diffusion_agent.TrainDiffusionAgent
|
||||
|
||||
name: avoid_m2_pre_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
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||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m2/train.pkl
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||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
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||||
mode: d57_r12 # M2, desired modes 5 and 7, required modes 1 and 2
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||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
denoising_steps: 20
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
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||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 15000
|
||||
batch_size: 16
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 15000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
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||||
save_model_freq: 1000
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||||
|
||||
model:
|
||||
_target_: model.diffusion.diffusion.DiffusionModel
|
||||
predict_epsilon: True
|
||||
denoised_clip_value: 1.0
|
||||
network:
|
||||
_target_: model.diffusion.mlp_diffusion.DiffusionMLP
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||||
time_dim: 16
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||||
mlp_dims: [512, 512, 512]
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||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
obs_dim: ${obs_dim}
|
||||
action_dim: ${action_dim}
|
||||
transition_dim: ${transition_dim}
|
||||
denoising_steps: ${denoising_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,62 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: avoid_m2_pre_gaussian_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m2/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d57_r12 # M2, desired modes 5 and 7, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 10000
|
||||
batch_size: 16
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gaussian.GaussianModel
|
||||
network:
|
||||
_target_: model.common.mlp_gaussian.Gaussian_MLP
|
||||
mlp_dims: [256, 256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
fixed_std: 0.1
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,64 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: avoid_m2_pre_gmm_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m2/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d57_r12 # M2, desired modes 5 and 7, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
num_modes: 5
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 10000
|
||||
batch_size: 32
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gmm.GMMModel
|
||||
network:
|
||||
_target_: model.common.mlp_gmm.GMM_MLP
|
||||
mlp_dims: [256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: False
|
||||
fixed_std: 0.1
|
||||
num_modes: ${num_modes}
|
||||
cond_dim: ${obs_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,70 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_diffusion_agent.TrainDiffusionAgent
|
||||
|
||||
name: avoid_m3_pre_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m3/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d58_r12 # M3, desired modes 5 and 8, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
denoising_steps: 20
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 15000
|
||||
batch_size: 16
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 15000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.diffusion.diffusion.DiffusionModel
|
||||
predict_epsilon: True
|
||||
denoised_clip_value: 1.0
|
||||
network:
|
||||
_target_: model.diffusion.mlp_diffusion.DiffusionMLP
|
||||
time_dim: 16
|
||||
mlp_dims: [512, 512, 512]
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
obs_dim: ${obs_dim}
|
||||
action_dim: ${action_dim}
|
||||
transition_dim: ${transition_dim}
|
||||
denoising_steps: ${denoising_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,62 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: avoid_m3_pre_gaussian_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m3/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d58_r12 # M3, desired modes 5 and 8, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 10000
|
||||
batch_size: 16
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gaussian.GaussianModel
|
||||
network:
|
||||
_target_: model.common.mlp_gaussian.Gaussian_MLP
|
||||
mlp_dims: [256, 256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: True
|
||||
fixed_std: 0.1
|
||||
cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
@@ -0,0 +1,64 @@
|
||||
defaults:
|
||||
- _self_
|
||||
hydra:
|
||||
run:
|
||||
dir: ${logdir}
|
||||
_target_: agent.pretrain.train_gaussian_agent.TrainGaussianAgent
|
||||
|
||||
name: avoid_m3_pre_gmm_mlp_ta${horizon_steps}
|
||||
logdir: ${oc.env:DPPO_LOG_DIR}/d3il-pretrain/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
|
||||
train_dataset_path: ${oc.env:DPPO_DATA_DIR}/d3il/avoid_m3/train.pkl
|
||||
|
||||
seed: 42
|
||||
device: cuda:0
|
||||
env: avoid
|
||||
mode: d58_r12 # M3, desired modes 5 and 8, required modes 1 and 2
|
||||
obs_dim: 4
|
||||
action_dim: 2
|
||||
transition_dim: ${action_dim}
|
||||
horizon_steps: 4
|
||||
cond_steps: 1
|
||||
num_modes: 5
|
||||
|
||||
wandb:
|
||||
entity: ${oc.env:DPPO_WANDB_ENTITY}
|
||||
project: d3il-${env}-pretrain
|
||||
run: ${now:%H-%M-%S}_${name}
|
||||
|
||||
train:
|
||||
n_epochs: 10000
|
||||
batch_size: 32
|
||||
learning_rate: 1e-4
|
||||
weight_decay: 1e-6
|
||||
lr_scheduler:
|
||||
first_cycle_steps: 10000
|
||||
warmup_steps: 100
|
||||
min_lr: 1e-5
|
||||
epoch_start_ema: 20
|
||||
update_ema_freq: 10
|
||||
save_model_freq: 1000
|
||||
|
||||
model:
|
||||
_target_: model.common.gmm.GMMModel
|
||||
network:
|
||||
_target_: model.common.mlp_gmm.GMM_MLP
|
||||
mlp_dims: [256, 256] # smaller MLP for less overfitting
|
||||
activation_type: ReLU
|
||||
residual_style: False
|
||||
fixed_std: 0.1
|
||||
num_modes: ${num_modes}
|
||||
cond_dim: ${obs_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
transition_dim: ${transition_dim}
|
||||
horizon_steps: ${horizon_steps}
|
||||
device: ${device}
|
||||
|
||||
ema:
|
||||
decay: 0.995
|
||||
|
||||
train_dataset:
|
||||
_target_: agent.dataset.sequence.StitchedActionSequenceDataset
|
||||
dataset_path: ${train_dataset_path}
|
||||
horizon_steps: ${horizon_steps}
|
||||
cond_steps: ${cond_steps}
|
||||
device: ${device}
|
||||
Reference in New Issue
Block a user