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>
108 lines
2.7 KiB
YAML
108 lines
2.7 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_idql_diffusion_agent.TrainIDQLDiffusionAgent
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name: ${env_name}_idql_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
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logdir: ${oc.env:DPPO_LOG_DIR}/gym-finetune/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
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normalization_path: ${oc.env:DPPO_DATA_DIR}/gym/${env_name}/normalization.npz
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seed: 42
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device: cuda:0
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env_name: hopper-medium-v2
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obs_dim: 11
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action_dim: 3
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denoising_steps: 10
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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: 10
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name: ${env_name}
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max_episode_steps: 1000
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reset_at_iteration: False
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save_video: False
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best_reward_threshold_for_success: 3
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wrappers:
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mujoco_locomotion_lowdim:
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normalization_path: ${normalization_path}
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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: ${oc.env:DPPO_WANDB_ENTITY}
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project: gym-${env_name}-scratch
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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: 0
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n_steps: 1000
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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-3
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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-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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# IDQL specific
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scale_reward_factor: 0.01
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eval_deterministic: True
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eval_sample_num: 10 # how many samples to score during eval
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critic_tau: 0.001 # rate of target q network update
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use_expectile_exploration: True
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buffer_size: 100000 # * n_envs
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replay_ratio: 128
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batch_size: 256
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model:
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_target_: model.diffusion.diffusion_idql.IDQLDiffusion
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# Sampling HPs
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min_sampling_denoising_std: 0.10
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randn_clip_value: 3
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#
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actor:
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_target_: model.diffusion.mlp_diffusion.DiffusionMLP
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horizon_steps: ${horizon_steps}
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action_dim: ${action_dim}
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cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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time_dim: 16
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mlp_dims: [512, 512, 512]
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activation_type: ReLU
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residual_style: True
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critic_q:
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_target_: model.common.critic.CriticObsAct
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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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action_dim: ${action_dim}
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action_steps: ${act_steps}
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critic_v:
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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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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} |