tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
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feat(message passing): used config value for message passing repetitions

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This commit is contained in:
Robin Meersman 2026-05-02 23:15:55 +02:00
parent 6114e36e27
commit 027b13b30f
4 changed files with 14 additions and 3 deletions
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2
configs/architecture/decentralized.yaml
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@ -29,4 +29,4 @@ critic:
message_passing_steps: 4 message_passing_steps: 4
# Connectivity topology (e.g., ring, fully_connected) # Connectivity topology (e.g., ring, fully_connected)
topology_type: "ring" topology_type: "fully_connected"

6
configs/morphology/2_arms_decentralized.yaml Normal file
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@ -0,0 +1,6 @@
# 2 Arms Morphology Configuration
segments_per_arm: [4, 0, 4, 0, 0]
use_p_control: true
use_torque_control: false
morph_mode: FULLY_CONNECTED

2
scripts/train.py
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@ -42,7 +42,7 @@ def main(dict_cfg: DictConfig):
base_dir=os.path.dirname(run_dir), base_dir=os.path.dirname(run_dir),
) )
logger = get_logger() logger = get_logger()
logger.set_level(logging.DEBUG) logger.set_level(logging.INFO)
logger.info(f"Hydra-initialized run: {run_name}") logger.info(f"Hydra-initialized run: {run_name}")
logger.info(f"Output directory: {run_dir}") logger.info(f"Output directory: {run_dir}")

7
src/brittle_star_project/trainers/PPOTrainer.py
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@ -221,10 +221,15 @@ def _get_action_and_value_noise(
adj_matrix: jnp.ndarray, adj_matrix: jnp.ndarray,
): ):
def apply_message_passing(h): def apply_message_passing(h):
assert message_passer is not None, (
"message passing shouldn't be performed if morphology mode is Centralized"
)
return message_passer.apply(agent_state.params["message_passer_params"], h, adj_matrix) return message_passer.apply(agent_state.params["message_passer_params"], h, adj_matrix)
hidden = apply_per_node(sensor, agent_state.params["sensor_params"], next_obs) hidden = apply_per_node(sensor, agent_state.params["sensor_params"], next_obs)
hidden = jax.vmap(apply_message_passing)(hidden) if message_passer is not None:
hidden = jax.vmap(apply_message_passing)(hidden)
hidden_critic = apply_shared( hidden_critic = apply_shared(
feature_extractor, agent_state.params["feature_extractor_params"], next_obs feature_extractor, agent_state.params["feature_extractor_params"], next_obs