1d04211666
* update from scratch configs * update gym pretraining configs - use fewer epochs * update robomimic pretraining configs - use fewer epochs * allow trajectory plotting in eval agent * add simple vit unet * update avoid pretraining configs - use fewer epochs * update furniture pretraining configs - use same amount of epochs as before * add robomimic diffusion unet pretraining configs * update robomimic finetuning configs - higher lr * add vit unet checkpoint urls * update pretraining and finetuning instructions as configs are updated
68 lines
1.7 KiB
YAML
68 lines
1.7 KiB
YAML
defaults:
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- _self_
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hydra:
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run:
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dir: ${logdir}
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_target_: agent.eval.eval_diffusion_agent.EvalDiffusionAgent
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name: ${env_name}_eval_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
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logdir: ${oc.env:DPPO_LOG_DIR}/furniture-eval/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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base_policy_path:
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normalization_path: ${oc.env:DPPO_DATA_DIR}/furniture/${env.specific.furniture}_${env.specific.randomness}/normalization.pth
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seed: 42
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device: cuda:0
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env_name: ${env.specific.furniture}_${env.specific.randomness}_dim
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obs_dim: 44
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action_dim: 10
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denoising_steps: 100
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cond_steps: 1
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horizon_steps: 16
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act_steps: 8
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use_ddim: True
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ddim_steps: 5
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n_steps: ${eval:'round(${env.max_episode_steps} / ${act_steps})'}
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render_num: 0
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env:
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n_envs: 1000
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name: ${env_name}
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env_type: furniture
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max_episode_steps: 1000
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best_reward_threshold_for_success: 2
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specific:
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headless: true
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furniture: round_table
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randomness: low
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normalization_path: ${normalization_path}
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obs_steps: ${cond_steps}
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act_steps: ${act_steps}
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sparse_reward: True
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model:
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_target_: model.diffusion.diffusion.DiffusionModel
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predict_epsilon: True
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denoised_clip_value: 1.0
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randn_clip_value: 3
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#
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use_ddim: ${use_ddim}
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ddim_steps: ${ddim_steps}
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network_path: ${base_policy_path}
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network:
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_target_: model.diffusion.unet.Unet1D
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diffusion_step_embed_dim: 16
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dim: 64
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dim_mults: [1, 2, 4]
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kernel_size: 5
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n_groups: 8
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smaller_encoder: False
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cond_predict_scale: True
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groupnorm_eps: 1e-4 # not important
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cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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action_dim: ${action_dim}
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horizon_steps: ${horizon_steps}
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obs_dim: ${obs_dim}
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action_dim: ${action_dim}
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denoising_steps: ${denoising_steps}
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device: ${device} |