chore: cleanup and HPC integration

configs/default_ppo.yaml

@@ -1,48 +0,0 @@
-# PPO Training Configuration Template
-#
-# This file provides an example configuration for PPO training.
-# Copy this file and modify it for your specific experiments.
-#
-# Usage:
-#   python src/train.py --config-path configs/my_config.yaml
-# Or override specific parameters:
-#   python src/train.py --learning-rate 0.001 --num-envs 32
-
-# Experiment settings
-exp_name: "brittle_star_ppo"
-seed: 1
-
-# Tracking settings
-track: false  # Set to true to enable WandB logging
-wandb_project_name: "PPO-Modularity"
-wandb_entity: "SEL3-2026-Groep-4"  # Set to your WandB username or team name
-
-# Model saving
-save_model: true
-checkpoint_frequency: 100  # Save checkpoint every N iterations (0 = no checkpoints)
-
-# Environment settings
-num_envs: 16
-
-# Training hyperparameters
-total_timesteps: 10000000
-learning_rate: 0.00025
-num_steps: 128
-anneal_lr: true
-
-# PPO specific
-gamma: 0.99
-gae_lambda: 0.95
-num_minibatches: 4
-update_epochs: 4
-norm_adv: true
-clip_coef: 0.1
-clip_vloss: true
-ent_coef: 0.01
-vf_coef: 0.5
-max_grad_norm: 0.5
-target_kl: null
-
-# Hardware
-cuda: true
-torch_deterministic: true

configs/dev_test.yaml

@@ -1,42 +0,0 @@
-# Quick Development/Testing Configuration
-#
-# Fast configuration for development and testing with short runs.
-
-# Experiment settings
-exp_name: "brittle_star_dev_test"
-seed: 123
-
-# Tracking settings - IMPORTANT: Set your own wandb_entity!
-track: true
-wandb_project_name: "PPO-Modularity-Dev"
-wandb_entity: "SEL3-2026-Groep-4"  # ⚠️  SET THIS TO YOUR WANDB USERNAME OR TEAM
-
-# Model saving
-save_model: true
-checkpoint_frequency: 10  # More frequent checkpoints for testing
-
-# Environment settings
-num_envs: 4  # Smaller for faster iteration
-
-# Training hyperparameters - Fast/testing
-total_timesteps: 100000  # Short run for testing
-learning_rate: 0.001  # Higher learning rate for faster learning
-num_steps: 64  # Shorter rollouts
-anneal_lr: true
-
-# PPO specific - Optimized for quick results
-gamma: 0.99
-gae_lambda: 0.95
-num_minibatches: 2
-update_epochs: 2  # Fewer epochs for speed
-norm_adv: true
-clip_coef: 0.1
-clip_vloss: true
-ent_coef: 0.02  # Higher entropy for exploration
-vf_coef: 0.5
-max_grad_norm: 0.5
-target_kl: null
-
-# Hardware
-cuda: true
-torch_deterministic: true

configs/example.yaml

@@ -1,4 +0,0 @@
-morphology:
-  segments_per_arm: [4, 4]
-  use_p_control: true
-  use_torque_control: false

configs/hpc/smoke_test.yaml

@@ -1,11 +0,0 @@
-# Minimal config to verify HPC setup is functional.
-# Run with: python scripts/train.py --config-path configs/hpc/smoke_test.yaml
-exp_name: "hpc_smoke_test"
-seed: 0
-track: false           # Test WandB integration
-capture_video: false  # No rendering for smoke test
-save_model: true      # Test the end-of-training save routine
-num_envs: 512
-total_timesteps: 65536
-num_steps: 128
-cuda: true

configs/hpc/wandb_expand.yaml

@@ -1,10 +0,0 @@
-exp_name: "explained_var_fun_more_steps"
-seed: 42
-track: true
-wandb_project_name: "LET-THERE-BE-MORE-LOGGING"
-wandb_entity: "SEL3-2026-Groep-4"
-
-num_envs: 16
-num_steps: 256
-total_timesteps: 50000
-cuda: true

configs/hpc/wandb_test.yaml

@@ -1,11 +0,0 @@
-# Configuration to verify WandB online tracking
-exp_name: "hpc_wandb_verification"
-seed: 42
-track: true               # Enabled for testing WandB
-wandb_project_name: "PPO-Modularity"
-wandb_entity: "SEL3-2026-Groep-4"
-
-num_envs: 128
-total_timesteps: 50000    # Short run for quick verification
-num_steps: 128
-cuda: true

configs/personal_template.yaml

@@ -1,40 +0,0 @@
-# Personal Configuration Example for Team Member
-#
-# Copy this template and customize for your personal experiments
-
-# Experiment settings - PERSONALIZE THESE
-exp_name: "YOUR_NAME_experiment_v1"  # ⚠️ Change YOUR_NAME 
-seed: 42
-
-# WandB settings - ⚠️ IMPORTANT: Set your credentials!
-track: true  # Enable WandB tracking
-wandb_project_name: "PPO-Modularity"
-wandb_entity: "SEL3-2026-Groep-4"  # ⚠️ CHANGE THIS to your WandB username/team
-
-# Quick experiment settings (modify as needed)
-total_timesteps: 500000  # 500K for quick results
-num_envs: 8
-learning_rate: 0.0005
-num_steps: 128
-
-# Model saving
-save_model: true  
-checkpoint_frequency: 25  # Save checkpoints frequently
-
-# Standard PPO settings (usually don't need to change)
-gamma: 0.99
-gae_lambda: 0.95
-num_minibatches: 4
-update_epochs: 4
-norm_adv: true
-clip_coef: 0.2
-clip_vloss: true
-ent_coef: 0.01
-vf_coef: 0.5
-max_grad_norm: 0.5
-target_kl: null
-anneal_lr: true
-
-# Hardware
-cuda: true
-torch_deterministic: true

configs/production_training.yaml

@@ -1,42 +0,0 @@
-# Production Training Configuration
-#
-# Full-scale training configuration for production runs
-# with wandb logging enabled.
-
-# Experiment settings
-exp_name: "brittle_star_production_training"
-seed: 42
-
-# Tracking
-track: true
-capture_video: false
-wandb_project_name: "PPO-Modularity"
-wandb_entity: "SEL3-2026-Groep-4"
-
-# Model saving
-save_model: true
-checkpoint_frequency: 100  # Save checkpoint every 100 iterations
-
-# Environment settings
-num_envs: 512
-
-# Training hyperparameters
-total_timesteps: 50000000
-num_steps: 256
-num_minibatches: 4
-update_epochs: 4
-
-learning_rate: 2.5e-4
-anneal_lr: true
-gamma: 0.99
-gae_lambda: 0.95
-clip_coef: 0.1
-clip_vloss: true
-ent_coef: 0.01
-vf_coef: 0.5
-max_grad_norm: 0.5
-target_kl: null
-
-# Hardware
-cuda: true
-torch_deterministic: true

scripts/hpc/train.pbs

@@ -63,11 +63,11 @@ elif [ -f "$PBS_O_WORKDIR/.env" ]; then
     export $(grep -v '^#' "$PBS_O_WORKDIR/.env" | xargs)
 fi
 
-# TODO Once experiments get serious, change the config
+# Run training using Hydra overrides
 python scripts/train.py \
-    --env-config-path configs/hpc/wandb_expand.yaml \
-    --hyperparameter-config-path configs/hpc/wandb_expand.yaml \
-    --run-dir "$SCRATCH_RUNDIR"
+    hydra.run.dir="$SCRATCH_RUNDIR" \
+    ppo=stable \
+    logging=wandb_enabled
 
 echo ">>> Staging out results to $DATA_RUNDIR..."
 cp -r "$SCRATCH_RUNDIR/." "$DATA_RUNDIR/"

src/brittle_star_project/dataclasses/PPOArgs.py

@@ -1,116 +0,0 @@
-from dataclasses import dataclass
-
-import jax
-
-
-@jax.tree_util.register_dataclass
-@dataclass
-class PPOArgs:
-    """
-    source: https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo_atari_envpool_xla_jax_scan.py
-    """
-
-    # path to environment config file, if None, use default config
-    env_config_path: str | None = None
-
-    # path to hyperparameter config file (yaml), if None, use default config
-    hyperparameter_config_path: str | None = None
-
-    # the name of this experiment
-    exp_name: str = "brittle_star_ppo"
-
-    # the directory to save the experiment results
-    run_dir: str | None = None
-
-    # seed of the experiment
-    seed: int = 1
-
-    # if toggled, `torch.backends.cudnn.deterministic=False`
-    torch_deterministic: bool = True
-
-    # if toggled, cuda will be enabled by default
-    cuda: bool = True
-
-    # if toggled, this experiment will be tracked with Weights and Biases
-    track: bool = False
-
-    # the wandb's project name
-    wandb_project_name: str = "PPO-Modularity"
-
-    # the entity (team) of wandb's project
-    wandb_entity: str | None = "SEL3-2026-Groep-4"
-
-    # whether to capture videos of the agent performances (check out `videos` folder)
-    capture_video: bool = False
-
-    # whether to save model into the `runs/{run_name}` folder
-    save_model: bool = True
-
-    # checkpoint frequency (in iterations, 0 = no intermediate checkpoints)
-    checkpoint_frequency: int = 100
-
-    # whether to upload the saved model to huggingface
-    upload_model: bool = False
-
-    # the user or org name of the model repository from the Hugging Face Hub
-    hf_entity: str = ""
-
-    # ==== Algorithm specific dataclasses ====
-
-    # total timesteps of the experiments
-    total_timesteps: int = 10000000
-
-    # the learning rate of the optimizer
-    learning_rate: float = 2.5e-4
-
-    # the number of parallel game environments
-    num_envs: int = 100
-
-    # the number of steps to run in each environment per policy rollout
-    num_steps: int = 128
-
-    # Toggle learning rate annealing for policy and value networks
-    anneal_lr: bool = True
-
-    # the discount factor gamma
-    gamma: float = 0.99
-
-    # the lambda for the general advantage estimation
-    gae_lambda: float = 0.95
-
-    # the number of mini-batches
-    num_minibatches: int = 4
-
-    # the K epochs to update the policy
-    update_epochs: int = 4
-
-    # Toggles advantages normalization
-    norm_adv: bool = True
-
-    # the surrogate clipping coefficient
-    clip_coef: float = 0.1
-
-    # Toggles whether or not to use a clipped loss for the value function, as per the paper.
-    clip_vloss: bool = True
-
-    # coefficient of the entropy
-    ent_coef: float = 0.01
-
-    # coefficient of the value function
-    vf_coef: float = 0.5
-
-    # the maximum norm for the gradient clipping
-    max_grad_norm: float = 0.5
-
-    # the target KL divergence threshold
-    target_kl: float | None = None
-
-    # ==== to be filled in runtime ====
-    # the batch size (computed in runtime)
-    batch_size: int = 0
-
-    # the mini-batch size (computed in runtime)
-    minibatch_size: int = 0
-
-    # the number of iterations (computed in runtime)
-    num_iterations: int = 0

src/brittle_star_project/dataclasses/__init__.py

@@ -1,8 +1,6 @@
-from .PPOArgs import PPOArgs
 from .EpisodeStatistics import EpisodeStatistics
 
 
 __all__ = [
-    "PPOArgs",
     "EpisodeStatistics",
 ]