fix: typos and nonexistent calls

configs/main_config.yaml

@@ -3,6 +3,7 @@
 # Sub-configs are loaded from the relative directories.
 
 defaults:
+  - brittle_star_config
   - experiment: base
   - logging: default
   - ppo: default

configs/ppo/smoke_test.yaml

@@ -0,0 +1,19 @@
+# Fast PPO Configuration
+# Lower timestep count for quick iterations/testing.
+
+learning_rate: 0.0005
+total_timesteps: 65536
+num_envs: 512
+num_steps: 128
+anneal_lr: true
+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

scripts/train.py

@@ -23,7 +23,7 @@ def make_env(cfg: BrittleStarConfig) -> BrittleStarJaxEnvWrapper:
 @hydra.main(config_path="../configs", config_name="main_config", version_base="1.3")
 def main(dict_cfg: DictConfig):
     # 1. Convert DictConfig to structured dataclass
-    cfg: BrittleStarConfig = OmegaConf.to_object(dict_cfg)
+    config: BrittleStarConfig = OmegaConf.to_object(dict_cfg)
 
     # 2. Setup run metadata
     # Hydra changes CWD to the output directory by default.
@@ -31,25 +31,25 @@ def main(dict_cfg: DictConfig):
     run_name = os.path.basename(run_dir)
 
     # 3. Initialize Logger
-    resolved_cfg_dict = OmegaConf.to_container(dict_cfg, resolve=True, throw_on_missing=True)
+    cfg_dict = OmegaConf.to_container(dict_cfg, resolve=True, throw_on_missing=True)
     init_logger(
         run_name=run_name,
-        config=resolved_cfg_dict,
-        project_name=cfg.logging.wandb_project_name,
-        entity=cfg.logging.wandb_entity,
+        config=cfg_dict,
+        project_name=config.logging.wandb_project_name,
+        entity=config.logging.wandb_entity,
         base_dir=os.path.dirname(run_dir),
-        use_wandb=cfg.logging.track,
+        use_wandb=config.logging.track,
     )
     logger = get_logger()
     logger.info(f"Hydra-initialized run: {run_name}")
     logger.info(f"Output directory: {run_dir}")
 
     # 4. Setup Environment and Torch
-    env = make_env(cfg)
-    torch.backends.cudnn.deterministic = cfg.experiment.torch_deterministic
+    env = make_env(config)
+    torch.backends.cudnn.deterministic = config.experiment.torch_deterministic
 
     # 5. Train - pass structured config directly
-    ppo_trainer = PPOTrainer(cfg, env, run_dir, run_name)
+    ppo_trainer = PPOTrainer(config, env, run_dir, run_name)
     ppo_trainer.train()
 
 

src/brittle_star_project/configs/config_architecture.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass, field
-from typing import List
+from typing import List, Optional
 
 
 @dataclass
@@ -18,14 +18,20 @@ class ArchitectureConfig:
     See docs/design/actor-critic.md for the full design rationale.
     """
 
-    # Input network: maps global observation to a hidden representation.
-    feature_extractor: LayerConfig = field(
-        default_factory=lambda: LayerConfig(hidden_dims=[64, 64], activation="tanh")
-    )
-    # Output network: maps the hidden representation to a scalar value estimate.
-    critic: LayerConfig = field(
-        default_factory=lambda: LayerConfig(hidden_dims=[], activation="tanh")
-    )
+    name: str = "base"
+
+    # Actor pipeline
+    sensor: Optional[LayerConfig] = None
+    propagator: Optional[LayerConfig] = None
+    motor: Optional[LayerConfig] = None
+
+    # Critic pipeline
+    feature_extractor: Optional[LayerConfig] = None
+    critic: Optional[LayerConfig] = None
+
+    # Decentralized
+    message_passing_steps: Optional[int] = None
+    topology_type: Optional[str] = None  # Supported values: "ring", "fully_connected"
 
 
 @dataclass
@@ -39,14 +45,7 @@ class CentralizedConfig(ArchitectureConfig):
     See docs/design/actor-critic.md for the full design rationale.
     """
 
-    # Input network: maps the global observation to a hidden state.
-    sensor: LayerConfig = field(
-        default_factory=lambda: LayerConfig(hidden_dims=[64, 64], activation="tanh")
-    )
-    # Output network: projects hidden state to (mean, log_std) over all joints.
-    motor: LayerConfig = field(
-        default_factory=lambda: LayerConfig(hidden_dims=[], activation="tanh")
-    )
+    name: str = "centralized"
 
 
 @dataclass
@@ -64,21 +63,4 @@ class DecentralizedConfig(ArchitectureConfig):
     full design rationale.
     """
 
-    # Input network: local observation -> initial hidden state per node.
-    sensor: LayerConfig = field(
-        default_factory=lambda: LayerConfig(hidden_dims=[64, 64], activation="tanh")
-    )
-    # Message-passing network: aggregates neighbour messages and updates hidden state.
-    propagator: LayerConfig = field(
-        default_factory=lambda: LayerConfig(hidden_dims=[64, 64], activation="tanh")
-    )
-    # Output network: final hidden state -> joint offset for this node only.
-    motor: LayerConfig = field(
-        default_factory=lambda: LayerConfig(hidden_dims=[], activation="tanh")
-    )
-
-    # Number of synchronous message-passing rounds per control step.
-    message_passing_steps: int = 1
-    # Graph topology used for neighbour connections.
-    # Supported values: "ring", "fully_connected".
-    topology_type: str = "ring"
+    name: str = "decentralized"

src/brittle_star_project/configs/register_configs.py

@@ -29,8 +29,8 @@ def register_configs() -> None:
     cs.store(group="ppo", name="base_ppo", node=PPOConfig)
 
     # Architecture variants — swap via CLI: architecture=decentralized
-    cs.store(group="architecture", name="centralized", node=CentralizedConfig)
-    cs.store(group="architecture", name="decentralized", node=DecentralizedConfig)
+    cs.store(group="architecture", name="centralized_schema", node=CentralizedConfig)
+    cs.store(group="architecture", name="decentralized_schema", node=DecentralizedConfig)
 
     # Environment configs
     cs.store(group="morphology", name="base_morphology", node=MorphologyConfig)

src/brittle_star_project/trainers/PPOTrainer.py

@@ -527,11 +527,11 @@ class PPOTrainer:
                 f"ETA {eta_str}"
             )
 
-            if getattr(self.config.experiment, "debug_sanity", False):
-                self.logger.log(
+            if getattr(self.cfg.experiment, "debug_sanity", False):
+                self.logger.info(
                     "\n[SANITY CHECK] Successfully completed 1 epoch of data collection and gradient updates."
                 )
-                self.logger.log("[SANITY CHECK] Gradients flowed without NaN. Exiting gracefully.")
+                self.logger.info("[SANITY CHECK] Gradients flowed without NaN. Exiting gracefully.")
                 break
 
         if self.logging_cfg.save_model:

src/experiment_logger/unified_logger.py

@@ -53,7 +53,7 @@ def init_logger(**kwargs) -> "UnifiedLogger":
     the output directories and set up all logging backends.
     """
     global _active_logger
-    logger = UnifiedLogger(_set_as_global=False, **kwargs)
+    logger = UnifiedLogger(**kwargs)
     _active_logger = logger
     return logger