defaults:
  - _self_
hydra:
  run:  
    dir: ${logdir}
_target_: agent.finetune.train_ppo_diffusion_agent.TrainPPODiffusionAgent

name: ${env_name}_ft_diffusion_unet_ta${horizon_steps}_td${denoising_steps}_tdf${ft_denoising_steps}
logdir: ${oc.env:DPPO_LOG_DIR}/robomimic-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
base_policy_path: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/transport/transport_pre_diffusion_unet_ta16_td20/2024-07-04_02-20-53/checkpoint/state_8000.pt
robomimic_env_cfg_path: cfg/robomimic/env_meta/${env_name}.json
normalization_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env_name}/normalization.npz

seed: 42
device: cuda:0
env_name: transport
obs_dim: 59
action_dim: 14
denoising_steps: 20
ft_denoising_steps: 10
cond_steps: 1
horizon_steps: 16
act_steps: 8

env:
  n_envs: 50
  name: ${env_name}
  best_reward_threshold_for_success: 1
  max_episode_steps: 800
  save_video: False
  wrappers:
    robomimic_lowdim:
      normalization_path: ${normalization_path}
      low_dim_keys: ['robot0_eef_pos',
                    'robot0_eef_quat',
                    'robot0_gripper_qpos',
                    "robot1_eef_pos",
                    "robot1_eef_quat",
                    "robot1_gripper_qpos",
                    'object'] # same order of preprocessed observations
    multi_step:
      n_obs_steps: ${cond_steps}
      n_action_steps: ${act_steps}
      max_episode_steps: ${env.max_episode_steps}
      reset_within_step: True

wandb:
  entity: ${oc.env:DPPO_WANDB_ENTITY}
  project: robomimic-${env_name}-finetune
  run: ${now:%H-%M-%S}_${name}

train:
  n_train_itr: 201
  n_critic_warmup_itr: 2
  n_steps: 400
  gamma: 0.999
  actor_lr: 2e-5
  actor_weight_decay: 0
  actor_lr_scheduler:
    first_cycle_steps: ${train.n_train_itr}
    warmup_steps: 10
    min_lr: 1e-4
  critic_lr: 1e-3
  critic_weight_decay: 0
  critic_lr_scheduler:
    first_cycle_steps: ${train.n_train_itr}
    warmup_steps: 10
    min_lr: 1e-3
  save_model_freq: 100
  val_freq: 10
  render:
    freq: 1
    num: 0
  # PPO specific
  reward_scale_running: True
  reward_scale_const: 1.0
  gae_lambda: 0.95
  batch_size: 10000
  update_epochs: 5
  vf_coef: 0.5
  target_kl: 1

model:
  _target_: model.diffusion.diffusion_ppo.PPODiffusion
  # HP to tune
  gamma_denoising: 0.99
  clip_ploss_coef: 0.01
  clip_ploss_coef_base: 0.001
  clip_ploss_coef_rate: 3
  randn_clip_value: 3
  min_sampling_denoising_std: 0.1
  min_logprob_denoising_std: 0.1
  #
  network_path: ${base_policy_path}
  actor:
    _target_: model.diffusion.unet.Unet1D
    diffusion_step_embed_dim: 16
    dim: 64
    dim_mults: [1, 2]
    kernel_size: 5
    n_groups: 8
    smaller_encoder: False
    cond_predict_scale: True
    cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
    action_dim: ${action_dim}
  critic:
    _target_: model.common.critic.CriticObs
    mlp_dims: [256, 256, 256]
    activation_type: Mish
    residual_style: True
    cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
  ft_denoising_steps: ${ft_denoising_steps}
  horizon_steps: ${horizon_steps}
  obs_dim: ${obs_dim}
  action_dim: ${action_dim}
  denoising_steps: ${denoising_steps}
  device: ${device}