107 lines
2.7 KiB
YAML
107 lines
2.7 KiB
YAML
# @package _global_
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seed: 100000
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horizon: 16
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n_obs_steps: 2
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n_action_steps: 8
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dataset_obs_steps: ${n_obs_steps}
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past_action_visible: False
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keypoint_visible_rate: 1.0
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eval_episodes: 50
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eval_freq: 5000
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save_freq: 5000
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log_freq: 250
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offline_steps: 200000
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online_steps: 0
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offline_prioritized_sampler: true
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override_dataset_stats:
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# TODO(rcadene, alexander-soare): should we remove image stats as well? do we use a pretrained vision model?
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observation.image:
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mean: [[[0.5]], [[0.5]], [[0.5]]] # (c,1,1)
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std: [[[0.5]], [[0.5]], [[0.5]]] # (c,1,1)
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# TODO(rcadene, alexander-soare): we override state and action stats to use the same as the pretrained model
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# from the original codebase, but we should remove these and train our own pretrained model
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observation.state:
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min: [13.456424, 32.938293]
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max: [496.14618, 510.9579]
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action:
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min: [12.0, 25.0]
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max: [511.0, 511.0]
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policy:
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name: diffusion
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pretrained_model_path:
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# Input / output structure.
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n_obs_steps: ${n_obs_steps}
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horizon: ${horizon}
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n_action_steps: ${n_action_steps}
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input_shapes:
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# TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
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observation.image: [3, 96, 96]
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observation.state: ["${env.state_dim}"]
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output_shapes:
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action: ["${env.action_dim}"]
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# Normalization / Unnormalization
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input_normalization_modes:
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observation.image: mean_std
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observation.state: min_max
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output_normalization_modes:
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action: min_max
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# Architecture / modeling.
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# Vision backbone.
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vision_backbone: resnet18
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crop_shape: [84, 84]
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crop_is_random: True
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use_pretrained_backbone: false
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use_group_norm: True
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spatial_softmax_num_keypoints: 32
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# Unet.
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down_dims: [512, 1024, 2048]
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kernel_size: 5
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n_groups: 8
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diffusion_step_embed_dim: 128
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use_film_scale_modulation: True
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# Noise scheduler.
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num_train_timesteps: 100
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beta_schedule: squaredcos_cap_v2
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beta_start: 0.0001
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beta_end: 0.02
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prediction_type: epsilon # epsilon / sample
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clip_sample: True
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clip_sample_range: 1.0
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# Inference
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num_inference_steps: 100
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# ---
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# TODO(alexander-soare): Remove these from the policy config.
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batch_size: 64
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grad_clip_norm: 10
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lr: 1.0e-4
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lr_scheduler: cosine
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lr_warmup_steps: 500
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adam_betas: [0.95, 0.999]
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adam_eps: 1.0e-8
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adam_weight_decay: 1.0e-6
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utd: 1
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use_ema: true
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ema_update_after_step: 0
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ema_min_alpha: 0.0
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ema_max_alpha: 0.9999
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ema_inv_gamma: 1.0
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ema_power: 0.75
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delta_timestamps:
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observation.image: "[i / ${fps} for i in range(1 - ${n_obs_steps}, 1)]"
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observation.state: "[i / ${fps} for i in range(1 - ${n_obs_steps}, 1)]"
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action: "[i / ${fps} for i in range(1 - ${n_obs_steps}, 1 - ${n_obs_steps} + ${policy.horizon})]"
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