v0.7 (#26)

* 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

cfg/furniture/eval/one_leg_low/eval_diffusion_mlp.yaml

@@ -7,7 +7,7 @@ _target_: agent.eval.eval_diffusion_agent.EvalDiffusionAgent
 
 name: ${env_name}_eval_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
 logdir: ${oc.env:DPPO_LOG_DIR}/furniture-eval/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
-base_policy_path: ${oc.env:DPPO_LOG_DIR}/furniture-pretrain/one_leg/one_leg_low_dim_pre_diffusion_mlp_ta8_td100/2024-07-22_20-01-16/checkpoint/state_8000.pt
+base_policy_path:
 normalization_path: ${oc.env:DPPO_DATA_DIR}/furniture/${env.specific.furniture}_${env.specific.randomness}/normalization.pth
 
 seed: 42

cfg/furniture/eval/one_leg_low/eval_diffusion_unet.yaml

@@ -0,0 +1,68 @@
+defaults:
+  - _self_
+hydra:
+  run:
+    dir: ${logdir}
+_target_: agent.eval.eval_diffusion_agent.EvalDiffusionAgent
+
+name: ${env_name}_eval_diffusion_mlp_ta${horizon_steps}_td${denoising_steps}
+logdir: ${oc.env:DPPO_LOG_DIR}/furniture-eval/${name}/${now:%Y-%m-%d}_${now:%H-%M-%S}_${seed}
+base_policy_path:
+normalization_path: ${oc.env:DPPO_DATA_DIR}/furniture/${env.specific.furniture}_${env.specific.randomness}/normalization.pth
+
+seed: 42
+device: cuda:0
+env_name: ${env.specific.furniture}_${env.specific.randomness}_dim
+obs_dim: 58
+action_dim: 10
+denoising_steps: 100
+cond_steps: 1
+horizon_steps: 16
+act_steps: 8
+use_ddim: True
+ddim_steps: 5
+
+n_steps: ${eval:'round(${env.max_episode_steps} / ${act_steps})'}
+render_num: 0
+
+env:
+  n_envs: 1000
+  name: ${env_name}
+  env_type: furniture
+  max_episode_steps: 700
+  best_reward_threshold_for_success: 1
+  specific:
+    headless: true
+    furniture: one_leg
+    randomness: low
+    normalization_path: ${normalization_path}
+    obs_steps: ${cond_steps}
+    act_steps: ${act_steps}
+    sparse_reward: True
+
+model:
+  _target_: model.diffusion.diffusion.DiffusionModel
+  predict_epsilon: True
+  denoised_clip_value: 1.0
+  randn_clip_value: 3
+  #
+  use_ddim: ${use_ddim}
+  ddim_steps: ${ddim_steps}
+  network_path: ${base_policy_path}
+  network:
+    _target_: model.diffusion.unet.Unet1D
+    diffusion_step_embed_dim: 16
+    dim: 64
+    dim_mults: [1, 2, 4]
+    kernel_size: 5
+    n_groups: 8
+    smaller_encoder: False
+    cond_predict_scale: True
+    groupnorm_eps: 1e-4 # not important
+    cond_dim: ${eval:'${obs_dim} * ${cond_steps}'}
+    action_dim: ${action_dim}
+  horizon_steps: ${horizon_steps}
+  obs_dim: ${obs_dim}
+  action_dim: ${action_dim}
+  denoising_steps: ${denoising_steps}
+  device: ${device}