dppo/cfg/d3il/pretrain/avoid_m2/pre_diffusion_mlp.yaml
2024-09-03 21:03:27 -04:00

70 lines
1.7 KiB
YAML

defaults:
- _self_
hydra:
run:
dir: ${logdir}
_target_: agent.pretrain.train_diffusion_agent.TrainDiffusionAgent
name: avoid_m2_pre_diffusion_mlp_ta${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_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}
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}