chore: cleanup and HPC integration

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",
 ]