0424a080c1
- Updated all WandB project names to use dppo- prefix for organization - Added flexible dev testing script for all environments - Created organized dev_tests directory for test scripts - Fixed MuJoCo compilation issues (added GCC compiler flags) - Documented Python 3.10 compatibility and Furniture-Bench limitation - Validated pre-training for Gym, Robomimic, D3IL environments - Updated experiment tracking with validation results - Enhanced README with troubleshooting and setup instructions
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_ppo_diffusion_agent.TrainPPODiffusionAgent
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name: ${env_name}_ppo_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}_tdf${ft_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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base_policy_path: ${oc.env:DPPO_LOG_DIR}/gym-pretrain/hopper-medium-v2_pre_diffusion_mlp_ta4_td20/2024-06-12_23-10-05/checkpoint/state_3000.pt
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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: 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: 40
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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 # success rate not relevant for gym tasks
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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: ${oc.env:DPPO_WANDB_ENTITY}
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project: dppo-gym-${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: 1000
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n_critic_warmup_itr: 0
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n_steps: 500
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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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# 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: 50000
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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: 0.99
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clip_ploss_coef: 0.01
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clip_ploss_coef_base: 0.01
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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.mlp_diffusion.DiffusionMLP
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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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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.CriticObs
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cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
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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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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} |