102 lines
2.5 KiB
YAML
102 lines
2.5 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_awr_diffusion_agent.TrainAWRDiffusionAgent
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name: ${env_name}_awr_diffusion_mlp_ta${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}
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base_policy_path: ${oc.env:DPPO_LOG_DIR}/gym-pretrain/halfcheetah-medium-v2_pre_diffusion_mlp_ta4_td20/2024-06-12_23-04-42/checkpoint/state_3000.pt
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normalization_path: ${oc.env:DPPO_DATA_DIR}/gym/${env_name}/normalization.npz
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device: cuda:0
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env_name: halfcheetah-medium-v2
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obs_dim: 17
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action_dim: 6
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transition_dim: ${action_dim}
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denoising_steps: 20
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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
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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}-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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# AWR specific
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scale_reward_factor: 0.01
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max_adv_weight: 100
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beta: 10
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buffer_size: 5000
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batch_size: 256
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replay_ratio: 64
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critic_update_ratio: 4
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model:
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_target_: model.diffusion.diffusion_awr.AWRDiffusion
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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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network_path: ${base_policy_path}
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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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transition_dim: ${transition_dim}
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cond_dim: ${obs_dim}
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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:
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_target_: model.common.critic.CriticObs
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obs_dim: ${obs_dim}
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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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horizon_steps: ${horizon_steps}
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obs_dim: ${obs_dim}
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action_dim: ${action_dim}
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transition_dim: ${transition_dim}
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denoising_steps: ${denoising_steps}
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device: ${device} |