tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
tdpeuter/2026SEL3-project-Brittle_Star_Controller_Modularity
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good luck cedric 🫡

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Robin Meersman 2026-04-30 19:32:20 +02:00
parent f8eb43004f
commit 1530e7d210
1 changed files with 26 additions and 35 deletions
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61
src/brittle_star_project/trainers/PPOTrainer.py
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@ -136,66 +136,53 @@ def _convert_obs_dict_to_array_morphology(obs_dict, morph_mode, segments_per_arm
@jax.jit @jax.jit
def _filter_and_flatten(o) -> jnp.ndarray: def _filter_and_flatten(o) -> jnp.ndarray:
# vmap feeds one env at a time — v has NO batch dim here
# shapes are e.g. (n_features,) or (n_nodes, feat)
values = [] values = []
for key in sorted(o.keys()): for key in sorted(o.keys()):
if key not in _ALLOWED_OBS_KEYS: if key not in _ALLOWED_OBS_KEYS:
continue continue
v = o[key] v = o[key]
if v.size == 0: if v.size == 0:
continue continue
# -------- CENTRALIZED -------- # -------- CENTRALIZED --------
if morph_mode == MorphMode.CENTRALIZED: if morph_mode == MorphMode.CENTRALIZED:
values.append(v.reshape(v.shape[0], -1)) values.append(v.reshape(1, -1)) # (1, feat)
continue continue
# -------- SPLIT TO SEGMENTS -------- # -------- SPLIT TO SEGMENTS --------
if key in _JOINT_SCALED_KEYS: if key in _JOINT_SCALED_KEYS:
if morph_mode == MorphMode.SEGMENT: if morph_mode == MorphMode.SEGMENT:
# special case: segment lvl and scale with joints,
# needs to split logic for center mlps and arms
# center:
# 3 joints per arm, each joint has 2 values
B = v.shape[0]
center_size = num_arms * 3 * 2 center_size = num_arms * 3 * 2
v_center = v[:, :center_size] v_center = v[:center_size].reshape(num_arms, 3 * 2) # (arms, 6)
v_center = v_center.reshape(B, num_arms, 3, 2) v_segs = v[center_size:].reshape(-1, 2) # (segs, 2)
values.append(jnp.concatenate([v_center, v_segs], axis=0)) # (arms+segs, ?)
v_segs = v[:, center_size:]
v_segs = v_segs.reshape(B, -1, 2)
v = jnp.concatenate([v_center.reshape(B, -1, 2), v_segs], axis=1)
values.append(v)
continue continue
v = v.reshape(v.shape[0], num_segments, 2) v = v.reshape(num_arms, -1) # (n_arms, 2)
elif key in _SEGMENT_SCALED_KEYS: elif key in _SEGMENT_SCALED_KEYS:
v = v[..., None] # (env, segments, 1) v = v[:, None] # (segments, 1)
else: else:
# global key, share with all # global key, broadcast to all nodes
if morph_mode == 3: # segment n_nodes = (num_segments + num_arms) if morph_mode == MorphMode.SEGMENT else num_arms
v = jnp.repeat(v[:, None, :], num_segments + num_arms, axis=1) v = jnp.repeat(v[None, :], n_nodes, axis=0) # (n_nodes, feat)
else: # ring or fully connect
v = jnp.repeat(v[:, None, :], num_arms, axis=1)
values.append(v)
continue
# -------- SEGMENT MODE -------- # -------- SEGMENT MODE --------
if morph_mode == MorphMode.SEGMENT: if morph_mode == MorphMode.SEGMENT:
values.append(v) values.append(v) # (n_nodes, feat)
continue continue
# -------- ARM MODE -------- # -------- ARM MODE --------
v = v.reshape(v.shape[0], num_arms, -1) v = v.reshape(num_arms, -1)
values.append(v) values.append(v) # (n_arms, feat)
return jnp.concatenate(values, axis=0) return jnp.concatenate(values, axis=-1) # (n_nodes, total_feat)
return jax.vmap(_filter_and_flatten)(obs_dict) return jax.vmap(_filter_and_flatten)(obs_dict)
# output: (batch, n_nodes, total_feat)
# Observation keys whose size scales with the number of joints (2 per segment). # Observation keys whose size scales with the number of joints (2 per segment).
@ -541,12 +528,15 @@ class PPOTrainer:
def _init_agent(self): def _init_agent(self):
self.logger.info("[AGENT]: Initializing agent...") self.logger.info("[AGENT]: Initializing agent...")
if (self.morph_mode == MorphMode.FULLY_CONNECTED) or (self.morph_mode == MorphMode.RING): match self.morph_mode:
needed_copies = sum(1 for s in self.segments_per_arm if s > 0) case MorphMode.CENTRALIZED:
else: needed_copies = 1
needed_copies = sum(self.segments_per_arm) + sum( case MorphMode.FULLY_CONNECTED | MorphMode.RING:
1 for s in self.segments_per_arm if s > 0 needed_copies = jnp.where(self.segments_per_arm > 0, 1, 0).sum()
) case MorphMode.SEGMENT:
needed_copies = (
self.segments_per_arm.sum() + jnp.where(self.segments_per_arm > 0, 1, 0).sum()
)
actor = Actor(action_dim=self.env.single_action_space.shape[0]) actor = Actor(action_dim=self.env.single_action_space.shape[0])
sensor = GenericDenseLayersWithActivation(layer_sizes=[300, 300, 300]) sensor = GenericDenseLayersWithActivation(layer_sizes=[300, 300, 300])
@ -580,6 +570,7 @@ class PPOTrainer:
actor_keys = jax.random.split(actor_key, self.needed_copies) actor_keys = jax.random.split(actor_key, self.needed_copies)
message_passer_keys = jax.random.split(message_passer_key, self.needed_copies) message_passer_keys = jax.random.split(message_passer_key, self.needed_copies)
# (needed_copies, 175)
sensor_params = jax.vmap(lambda k: self.sensor.init(k, sample_obs))(sensor_keys) sensor_params = jax.vmap(lambda k: self.sensor.init(k, sample_obs))(sensor_keys)
single_sensor_param = jax.tree.map(lambda x: x[0], sensor_params) single_sensor_param = jax.tree.map(lambda x: x[0], sensor_params)