e1ef4ca1cf
* move ema update within pretraining epoch * update pretraining ema configs * add lift and can epoch 8000 checkpoint url * add note about EMA issue in pretraining instruction
115 lines
3.0 KiB
YAML
115 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_ibrl_agent.TrainIBRLAgent
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name: ${env_name}_ibrl_mlp_ta${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: ${oc.env:DPPO_LOG_DIR}/robomimic-pretrain/square_pre_gaussian_mlp_ta1/2024-10-08_20-52-42_0/checkpoint/state_5000.pt
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robomimic_env_cfg_path: cfg/robomimic/env_meta/${env_name}.json
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normalization_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env_name}-ph/normalization.npz
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offline_dataset_path: ${oc.env:DPPO_DATA_DIR}/robomimic/${env_name}-ph/train.npz
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seed: 42
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device: cuda:0
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env_name: square
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obs_dim: 23
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action_dim: 7
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cond_steps: 1
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horizon_steps: 1
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act_steps: 1
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env:
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n_envs: 1
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name: ${env_name}
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max_episode_steps: 350 # IBRL uses 300
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reset_at_iteration: False
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save_video: False
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best_reward_threshold_for_success: 1
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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: ${oc.env:DPPO_WANDB_ENTITY}
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project: ibrl-${env_name}
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run: ${now:%H-%M-%S}_${name}
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train:
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n_train_itr: 1000000
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n_steps: 1
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gamma: 0.99
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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: 1000
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warmup_steps: 10
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min_lr: 1e-4
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critic_lr: 1e-4
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critic_weight_decay: 0
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critic_lr_scheduler:
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first_cycle_steps: 1000
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warmup_steps: 10
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min_lr: 1e-4
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save_model_freq: 50000
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val_freq: 10000
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render:
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freq: 10000
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num: 0
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log_freq: 200
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# IBRL specific
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batch_size: 256
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target_ema_rate: 0.01
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scale_reward_factor: 1
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critic_num_update: 3
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buffer_size: 400000
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n_eval_episode: 40
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n_explore_steps: 0
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update_freq: 2
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model:
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_target_: model.rl.gaussian_ibrl.IBRL_Gaussian
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randn_clip_value: 3
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n_critics: 5
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soft_action_sample: True
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soft_action_sample_beta: 10
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network_path: ${base_policy_path}
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actor:
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_target_: model.common.mlp_gaussian.Gaussian_MLP
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mlp_dims: [1024, 1024, 1024]
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activation_type: ReLU
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dropout: 0.5
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fixed_std: 0.1
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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.CriticObsAct
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mlp_dims: [1024, 1024, 1024]
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activation_type: ReLU
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use_layernorm: True
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double_q: False # use ensemble
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cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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action_dim: ${action_dim}
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action_steps: ${act_steps}
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horizon_steps: ${horizon_steps}
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device: ${device}
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offline_dataset:
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_target_: agent.dataset.sequence.StitchedSequenceQLearningDataset
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dataset_path: ${offline_dataset_path}
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horizon_steps: ${horizon_steps}
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cond_steps: ${cond_steps}
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device: ${device}
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max_n_episodes: 100 |