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
148 lines
3.8 KiB
YAML
148 lines
3.8 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.finetune.train_ppo_gaussian_img_agent.TrainPPOImgGaussianAgent
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name: ${env_name}_ft_gaussian_mlp_img_ta${horizon_steps}
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logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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base_policy_path: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/can/can_pre_gaussian_mlp_img_ta4/2024-07-28_21-54-40/checkpoint/state_1000.pt
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robomimic_env_cfg_path: cfg/robomimic/env_meta/${env_name}-img.json
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normalization_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env_name}-img/normalization.npz
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seed: 42
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device: cuda:0
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env_name: can
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obs_dim: 9
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action_dim: 7
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cond_steps: 1
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img_cond_steps: 1
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horizon_steps: 4
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act_steps: 4
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env:
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n_envs: 50
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name: ${env_name}
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best_reward_threshold_for_success: 1
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max_episode_steps: 300
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save_video: False
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use_image_obs: True
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wrappers:
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robomimic_image:
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normalization_path: ${normalization_path}
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low_dim_keys: ['robot0_eef_pos',
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'robot0_eef_quat',
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'robot0_gripper_qpos']
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image_keys: ['robot0_eye_in_hand_image']
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shape_meta: ${shape_meta}
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multi_step:
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n_obs_steps: ${cond_steps}
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n_action_steps: ${act_steps}
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max_episode_steps: ${env.max_episode_steps}
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reset_within_step: True
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shape_meta:
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obs:
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rgb:
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shape: [3, 96, 96]
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state:
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shape: [9]
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action:
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shape: [7]
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wandb:
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entity: ${oc.env:DPPO_WANDB_ENTITY}
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project: robomimic-${env_name}-finetune
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run: ${now:%H-%M-%S}_${name}
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train:
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n_train_itr: 151
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n_critic_warmup_itr: 2
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n_steps: 300
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gamma: 0.999
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augment: True
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grad_accumulate: 5
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actor_lr: 1e-4
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actor_weight_decay: 0
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actor_lr_scheduler:
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first_cycle_steps: ${train.n_train_itr}
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warmup_steps: 10
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min_lr: 1e-4
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critic_lr: 1e-3
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critic_weight_decay: 0
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critic_lr_scheduler:
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first_cycle_steps: ${train.n_train_itr}
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warmup_steps: 10
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min_lr: 1e-3
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save_model_freq: 100
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val_freq: 10
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render:
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freq: 1
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num: 0
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# PPO specific
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reward_scale_running: True
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reward_scale_const: 1.0
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gae_lambda: 0.95
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batch_size: 1500
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logprob_batch_size: 1000
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update_epochs: 10
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vf_coef: 0.5
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target_kl: 1
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model:
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_target_: model.rl.gaussian_ppo.PPO_Gaussian
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clip_ploss_coef: 0.01
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randn_clip_value: 3
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network_path: ${base_policy_path}
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actor:
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_target_: model.common.mlp_gaussian.Gaussian_VisionMLP
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backbone:
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_target_: model.common.vit.VitEncoder
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obs_shape: ${shape_meta.obs.rgb.shape}
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num_channel: ${eval:'3 * ${img_cond_steps}'} # each image patch is history concatenated
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img_h: ${shape_meta.obs.rgb.shape[1]}
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img_w: ${shape_meta.obs.rgb.shape[2]}
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cfg:
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patch_size: 8
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depth: 1
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embed_dim: 128
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num_heads: 4
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embed_style: embed2
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embed_norm: 0
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augment: False
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spatial_emb: 128
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mlp_dims: [512, 512, 512]
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residual_style: True
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fixed_std: 0.1
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learn_fixed_std: True
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std_min: 0.01
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std_max: 0.2
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img_cond_steps: ${img_cond_steps}
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cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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horizon_steps: ${horizon_steps}
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action_dim: ${action_dim}
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critic:
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_target_: model.common.critic.ViTCritic
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spatial_emb: 128
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augment: False
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backbone:
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_target_: model.common.vit.VitEncoder
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obs_shape: ${shape_meta.obs.rgb.shape}
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num_channel: ${eval:'3 * ${img_cond_steps}'} # each image patch is history concatenated
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img_h: ${shape_meta.obs.rgb.shape[1]}
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img_w: ${shape_meta.obs.rgb.shape[2]}
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cfg:
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patch_size: 8
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depth: 1
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embed_dim: 128
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num_heads: 4
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embed_style: embed2
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embed_norm: 0
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img_cond_steps: ${img_cond_steps}
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mlp_dims: [256, 256, 256]
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activation_type: Mish
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residual_style: True
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cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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horizon_steps: ${horizon_steps}
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device: ${device} |