1d04211666
* update from scratch configs * update gym pretraining configs - use fewer epochs * update robomimic pretraining configs - use fewer epochs * allow trajectory plotting in eval agent * add simple vit unet * update avoid pretraining configs - use fewer epochs * update furniture pretraining configs - use same amount of epochs as before * add robomimic diffusion unet pretraining configs * update robomimic finetuning configs - higher lr * add vit unet checkpoint urls * update pretraining and finetuning instructions as configs are updated
61 lines
1.8 KiB
YAML
61 lines
1.8 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.eval.eval_diffusion_agent.EvalDiffusionAgent
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name: ${env_name}_eval_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
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logdir: ${oc.env:DPPO_LOG_DIR}/gym-eval/${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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cond_steps: 1
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horizon_steps: 4
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act_steps: 4
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n_steps: 250 # each episode can take maximum (max_episode_steps / act_steps, =250 right now) steps but may finish earlier in gym. We only count episodes finished within n_steps for evaluation.
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render_num: 0
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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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model:
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_target_: model.diffusion.diffusion.DiffusionModel
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predict_epsilon: True
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denoised_clip_value: 1.0
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#
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network_path: ${base_policy_path}
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network:
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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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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} |