Reinforcement Learning
sub-jepa / subjepa /cube /config.yml
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output_model_name: lewm
subdir: ${hydra:job.id}
save_dir: xxx
num_workers: 8
train_split: 0.9
seed: 3072
img_size: 224
patch_size: 14
encoder_scale: tiny
dump_object: true
trainer:
max_epochs: 10
devices: 1
accelerator: gpu
precision: bf16
gradient_clip_val: 1.0
loader:
batch_size: 128
num_workers: ${num_workers}
persistent_workers: true
prefetch_factor: 3
pin_memory: true
wm:
type: lewm
history_size: 3
num_preds: 1
embed_dim: 192
sigreg_subspaces: 32
sigreg_subspace_dim: 6
sigreg_init_mode: 2
action_dim: 5
observation_dim: 28
proprio_dim: 19
predictor:
depth: 6
heads: 16
mlp_dim: 2048
dim_head: 64
dropout: 0.1
emb_dropout: 0.0
loss:
sigreg:
weight: 0.09
theta: 0.0
kwargs:
knots: 17
num_proj: 1024
optimizer:
type: AdamW
lr: 5.0e-05
weight_decay: 0.001
wandb:
enabled: true
config:
entity: xxx
project: lewm
name: ${output_model_name}
id: ${subdir}
resume: allow
log_model: false
data:
dataset:
name: cube_single_expert
num_steps: ${eval:'${wm.num_preds} + ${wm.history_size}'}
frameskip: 5
keys_to_load:
- pixels
- action
- observation
- proprio
keys_to_cache:
- action
- observation
keys_to_merge:
proprio: proprio