dc8e0c9edc
* Sampling over both env and denoising steps in DPPO updates (#13) * sample one from each chain * full random sampling * Add Proficient Human (PH) Configs and Pipeline (#16) * fix missing cfg * add ph config * fix how terminated flags are added to buffer in ibrl * add ph config * offline calql for 1M gradient updates * bug fix: number of calql online gradient steps is the number of new transitions collected * add sample config for DPPO with ta=1 * Sampling over both env and denoising steps in DPPO updates (#13) * sample one from each chain * full random sampling * fix diffusion loss when predicting initial noise * fix dppo inds * fix typo * remove print statement --------- Co-authored-by: Justin M. Lidard <jlidard@neuronic.cs.princeton.edu> Co-authored-by: allenzren <allen.ren@princeton.edu> * update robomimic configs * better calql formulation * optimize calql and ibrl training * optimize data transfer in ppo agents * add kitchen configs * re-organize config folders, rerun calql and rlpd * add scratch gym locomotion configs * add kitchen installation dependencies * use truncated for termination in furniture env * update furniture and gym configs * update README and dependencies with kitchen * add url for new data and checkpoints * update demo RL configs * update batch sizes for furniture unet configs * raise error about dropout in residual mlp * fix observation bug in bc loss --------- Co-authored-by: Justin Lidard <60638575+jlidard@users.noreply.github.com> Co-authored-by: Justin M. Lidard <jlidard@neuronic.cs.princeton.edu>
121 lines
3.1 KiB
YAML
121 lines
3.1 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: ${oc.env:DPPO_LOG_DIR}/furniture-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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base_policy_path: ${oc.env:DPPO_LOG_DIR}/furniture-pretrain/lamp/lamp_low_dim_pre_diffusion_unet_ta16_td100/2024-07-04_02-16-48/checkpoint/state_8000.pt
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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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ft_denoising_steps: 5
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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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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: lamp
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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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wandb:
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entity: ${oc.env:DPPO_WANDB_ENTITY}
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project: furniture-${env.specific.furniture}-${env.specific.randomness}-finetune
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run: ${now:%H-%M-%S}_${name}
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train:
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n_train_itr: 1000
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n_critic_warmup_itr: 1
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n_steps: ${eval:'round(${env.max_episode_steps} / ${act_steps})'}
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gamma: 0.999
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actor_lr: 1e-5
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actor_weight_decay: 0
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actor_lr_scheduler:
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first_cycle_steps: 10000
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warmup_steps: 10
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min_lr: 1e-6
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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: 10000
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warmup_steps: 10
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min_lr: 1e-3
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save_model_freq: 50
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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: 40000
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update_epochs: 5
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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: 0.9
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clip_ploss_coef: 0.001
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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: 0.04
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#
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use_ddim: ${use_ddim}
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ddim_steps: ${ft_denoising_steps}
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learn_eta: False
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eta:
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base_eta: 1
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input_dim: ${obs_dim}
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mlp_dims: [256, 256]
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action_dim: ${action_dim}
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min_eta: 0.1
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max_eta: 1.0
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_target_: model.diffusion.eta.EtaFixed
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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: 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
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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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cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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mlp_dims: [512, 512, 512]
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activation_type: Mish
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residual_style: True
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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} |