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
114 lines
3.0 KiB
YAML
114 lines
3.0 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_diffusion_agent.TrainPPODiffusionAgent
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name: ${env_name}_ft_diffusion_unet_ta${horizon_steps}_td${denoising_steps}_tdf${ft_denoising_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: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/lift/lift_pre_diffusion_unet_ta4_td20/2024-06-29_02-49-45/checkpoint/state_8000.pt
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robomimic_env_cfg_path: cfg/robomimic/env_meta/${env_name}.json
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normalization_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env_name}/normalization.npz
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seed: 42
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device: cuda:0
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env_name: lift
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obs_dim: 19
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action_dim: 7
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denoising_steps: 20
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ft_denoising_steps: 10
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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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wrappers:
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robomimic_lowdim:
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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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'object'] # same order of preprocessed observations
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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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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: 81
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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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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: 7500
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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.diffusion.diffusion_ppo.PPODiffusion
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# HP to tune
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gamma_denoising: 0.99
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clip_ploss_coef: 0.01
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clip_ploss_coef_base: 0.001
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clip_ploss_coef_rate: 3
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randn_clip_value: 3
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min_sampling_denoising_std: 0.1
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min_logprob_denoising_std: 0.1
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#
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network_path: ${base_policy_path}
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actor:
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_target_: model.diffusion.unet.Unet1D
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diffusion_step_embed_dim: 16
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dim: 40
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dim_mults: [1, 2]
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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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cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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action_dim: ${action_dim}
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critic:
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_target_: model.common.critic.CriticObs
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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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ft_denoising_steps: ${ft_denoising_steps}
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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} |