feat: config setup for lazy bums
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6 changed files with 134 additions and 2 deletions
95
configs/best_config_so_far.yaml
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95
configs/best_config_so_far.yaml
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architecture:
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critic:
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activation: tanh
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hidden_dims: []
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feature_extractor:
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activation: tanh
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hidden_dims:
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- 300
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- 300
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- 300
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message_passing_steps: null
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motor:
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activation: tanh
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hidden_dims: []
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name: centralized
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propagator: null
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sensor:
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activation: tanh
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hidden_dims:
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- 300
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- 300
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- 300
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topology_type: null
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arena:
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attach_target: true
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sand_ground_color: true
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size:
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- 10.0
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- 5.0
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wall_height: 1.5
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wall_thickness: 0.1
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environment:
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camera_ids:
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- 0
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- 1
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joint_randomization_noise_scale: 0.0
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light_perlin_noise_scale: 0
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num_physics_steps_per_control_step: 10
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render_size:
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- 480
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- 640
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simulation_time: 5000.0
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target_distance: 3.0
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task: !!python/object/apply:brittle_star_project.environment.env_types.Task
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- directed_locomotion
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time_scale: 2
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experiment:
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base_run_dir: runs
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cuda: true
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debug_sanity: false
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exp_name: lr lowered
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seed: 42
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torch_deterministic: true
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logging:
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capture_video: false
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checkpoint_frequency: 100
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hf_entity: ''
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save_checkpoints: true
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save_model: true
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track: true
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upload_checkpoints: false
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upload_final_model: true
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wandb_entity: SEL3-2026-Groep-4
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wandb_project_name: Reducing randomness
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morphology:
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segments_per_arm:
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- 4
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- 0
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- 4
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- 0
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- 0
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use_p_control: true
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use_torque_control: false
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ppo:
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anneal_lr: true
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clip_coef: 0.2
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clip_vloss: true
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ent_coef: 0.001
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gae_lambda: 0.95
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gamma: 0.99
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learning_rate: 0.0001
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max_grad_norm: 0.5
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norm_adv: true
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num_envs: 32
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num_minibatches: 32
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num_steps: 64
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target_kl: 0.02
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total_timesteps: 1228800
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update_epochs: 4
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vf_coef: 1.0
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simulation:
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backend: !!python/object/apply:brittle_star_project.environment.env_types.Backend
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- MJX
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model_path: null
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model_type: random
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2
configs/environment/dir_loc_further.yaml
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2
configs/environment/dir_loc_further.yaml
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simulation_time: 50000.0
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target_distance: 3.0
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6
configs/experiment/long_2arm.yaml
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6
configs/experiment/long_2arm.yaml
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# Testing chicken dinner 4 but further distance.
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exp_name: "long2arm"
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seed: 123
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torch_deterministic: true
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cuda: true
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10
configs/logging/pushing_long.yaml
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10
configs/logging/pushing_long.yaml
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capture_video: false
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checkpoint_frequency: 100
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hf_entity: ''
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save_checkpoints: true
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save_model: true
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track: true
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upload_checkpoints: false
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upload_final_model: true
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wandb_entity: "SEL3-2026-Groep-4"
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wandb_project_name: "Pushing our best found config"
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16
configs/ppo/chickendinnerwinner.yaml
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16
configs/ppo/chickendinnerwinner.yaml
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anneal_lr: true
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clip_coef: 0.2
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clip_vloss: true
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ent_coef: 0.001
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gae_lambda: 0.95
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gamma: 0.99
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learning_rate: 0.0001
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max_grad_norm: 0.5
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norm_adv: true
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num_envs: 32
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num_minibatches: 32
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num_steps: 64
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target_kl: 0.02
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total_timesteps: 12288000
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update_epochs: 4
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vf_coef: 1.0
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@ -66,8 +66,11 @@ fi
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# Run training using Hydra overrides
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python scripts/train.py \
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hydra.run.dir="$SCRATCH_RUNDIR" \
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ppo=stable \
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logging=hpc
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environment = dir_loc_further \
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experiment = long_2arm \
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ppo=chickendinnerwinner \
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logging=pushing_long
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echo ">>> Staging out results to $DATA_RUNDIR..."
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cp -r "$SCRATCH_RUNDIR/." "$DATA_RUNDIR/"
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