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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 | |