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defaults:
- base_pytorch_algo
# dataset-dependent configurations
x_shape: ${dataset.observation_shape}
frame_stack: 1
frame_skip: 1
data_mean: ${dataset.data_mean}
data_std: ${dataset.data_std}
external_cond_dim: 0 #${dataset.action_dim}
context_frames: ${dataset.context_length}
# training hyperparameters
weight_decay: 1e-4
warmup_steps: 10000
optimizer_beta: [0.9, 0.999]
# diffusion-related
uncertainty_scale: 1
guidance_scale: 0.0
chunk_size: 1 # -1 for full trajectory diffusion, number to specify diffusion chunk size
scheduling_matrix: autoregressive
noise_level: random_all
causal: True
diffusion:
# training
objective: pred_x0
beta_schedule: cosine
schedule_fn_kwargs: {}
clip_noise: 20.0
use_snr: False
use_cum_snr: False
use_fused_snr: False
snr_clip: 5.0
cum_snr_decay: 0.98
timesteps: 1000
# sampling
sampling_timesteps: 50 # fixme, numer of diffusion steps, should be increased
ddim_sampling_eta: 1.0
stabilization_level: 10
# architecture
architecture:
network_size: 64
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