WIP: stochastic hill climber

experiments/phenotype_genotype_map/myg.json

@@ -0,0 +1,96 @@
+{
+  "cortex": {
+    "id": 0.8543980322495442,
+    "sensor_ids": [
+      5.685640276474605e-10
+    ],
+    "actuator_ids": [
+      5.685640276474598e-10
+    ],
+    "neuron_ids": [
+      0.15322756084303968,
+      0.7120745036860967,
+      0.024186022052107514
+    ]
+  },
+  "sensor": {
+    "id": 5.685640276474605e-10,
+    "name": "xor_GetInput",
+    "vector_length": 2,
+    "cx_id": 0.8543980322495442,
+    "fanout_ids": [
+      0.15322756084303968,
+      0.7120745036860967
+    ]
+  },
+  "actuator": {
+    "id": 5.685640276474598e-10,
+    "name": "xor_SendOutput",
+    "vector_length": 1,
+    "cx_id": 0.8543980322495442,
+    "fanin_ids": [
+      0.024186022052107514
+    ]
+  },
+  "neurons": [
+    {
+      "id": 0.15322756084303968,
+      "layer_index": 0,
+      "cx_id": 0.8543980322495442,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 5.685640276474605e-10,
+          "weights": [
+            -1.463374592873151,
+            -0.941866321100616
+          ]
+        }
+      ],
+      "output_ids": [
+        0.32112185541392557
+      ]
+    },
+    {
+      "id": 0.7120745036860967,
+      "layer_index": 0,
+      "cx_id": 0.8543980322495442,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 5.685640276474605e-10,
+          "weights": [
+            1.8732878918890417,
+            1.4545727537289697
+          ]
+        }
+      ],
+      "output_ids": [
+        0.32112185541392557
+      ]
+    },
+    {
+      "id": 0.024186022052107514,
+      "layer_index": 1,
+      "cx_id": 0.8543980322495442,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 0.15322756084303968,
+          "weights": [
+            -1.3198353764897197
+          ]
+        },
+        {
+          "input_id": 0.7120745036860967,
+          "weights": [
+            -1.8234720470310797
+          ]
+        }
+      ],
+      "output_ids": [
+        5.685640276474598e-10
+      ]
+    }
+  ]
+}

experiments/phenotype_genotype_map/network.dot

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+digraph G {
+  rankdir=TB;
+  node [fontsize=10];
+  subgraph cluster_sensors { label="Sensors"; style=dashed;
+    "S0.000000" [label="xor_GetInput", shape=box];
+  }
+  subgraph cluster_L0 { label="Layer 0"; style=rounded;
+    "N0.153228" [label="N0", shape=circle];
+    "N0.712075" [label="N0", shape=circle];
+  }
+  subgraph cluster_L1 { label="Layer 1"; style=rounded;
+    "N0.024186" [label="N1", shape=circle];
+  }
+  subgraph cluster_actuators { label="Actuators"; style=dashed;
+    "A0.000000" [label="xor_SendOutput", shape=diamond];
+  }
+  "S0.000000" -> "N0.153228" [label="w×2"];
+  "S0.000000" -> "N0.712075" [label="w×2"];
+  "N0.153228" -> "N0.024186" [label="w×1"];
+  "N0.712075" -> "N0.024186" [label="w×1"];
+  "N0.024186" -> "A0.000000" [label="out"];
+}

experiments/phenotype_genotype_map/visualizer.py

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+import json
+
+
+def load_geno(p="myg.json"):
+    with open(p) as f:
+        return json.load(f)
+
+
+def to_dot(geno: dict) -> str:
+    sensors = []
+    if "sensors" in geno:
+        sensors = geno["sensors"]
+    elif "sensor" in geno:
+        sensors = [geno["sensor"]]
+
+    actuators = []
+    if "actuators" in geno:
+        actuators = geno["actuators"]
+    elif "actuator" in geno:
+        actuators = [geno["actuator"]]
+
+    neurons = geno["neurons"]
+
+    lines = []
+    lines.append('digraph G {')
+    lines.append('  rankdir=TB;')  # top-to-bottom
+    lines.append('  node [fontsize=10];')
+
+    lines.append('  subgraph cluster_sensors { label="Sensors"; style=dashed;')
+    for s in sensors:
+        lines.append(f'    "S{float(s["id"]):.6f}" [label="{s.get("name", "sensor")}", shape=box];')
+    lines.append('  }')
+
+    by_layer = {}
+    for n in neurons:
+        by_layer.setdefault(n["layer_index"], []).append(n)
+
+    for li in sorted(by_layer.keys()):
+        lines.append(f'  subgraph cluster_L{li} {{ label="Layer {li}"; style=rounded;')
+        for n in by_layer[li]:
+            lines.append(f'    "N{float(n["id"]):.6f}" [label="N{li}", shape=circle];')
+        lines.append('  }')
+
+    lines.append('  subgraph cluster_actuators { label="Actuators"; style=dashed;')
+    for a in actuators:
+        lines.append(f'    "A{float(a["id"]):.6f}" [label="{a.get("name", "act")}", shape=diamond];')
+    lines.append('  }')
+
+    for n in neurons:
+        to_id = f'N{float(n["id"]):.6f}'
+        for iw in n["input_weights"]:
+            src = iw["input_id"]
+            wcount = len(iw["weights"])
+            is_sensor = any(abs(src - s["id"]) < 1e-9 for s in sensors)
+            from_id = f'S{float(src):.6f}' if is_sensor else f'N{float(src):.6f}'
+            lines.append(f'  "{from_id}" -> "{to_id}" [label="w×{wcount}"];')
+
+    for a in actuators:
+        to_id = f'A{float(a["id"]):.6f}'
+        for src in a.get("fanin_ids", []):
+            lines.append(f'  "N{float(src):.6f}" -> "{to_id}" [label="out"];')
+
+    lines.append('}')
+    return "\n".join(lines)
+
+
+if __name__ == "__main__":
+    geno = load_geno("myg.json")
+    dot = to_dot(geno)
+    with open("network.dot", "w") as f:
+        f.write(dot)
+    print("Wrote network.dot")

experiments/stochastic_hillclimber/actors/actor.py

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+import asyncio
+
+
+class Actor:
+    def __init__(self, name: str):
+        self.name = name
+        self.inbox = asyncio.Queue()
+
+    async def send(self, msg):
+        await self.inbox.put(msg)
+
+    async def run(self):
+        raise NotImplementedError

experiments/stochastic_hillclimber/actors/actuator.py

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+# actors/actuator.py
+import asyncio
+
+from actor import Actor
+
+
+class Actuator(Actor):
+    def __init__(self, aid, cx_pid, name, fanin_ids, expect_count, scape=None):
+        super().__init__(f"Actuator-{aid}")
+        self.aid = aid
+        self.cx_pid = cx_pid
+        self.aname = name
+        self.fanin_ids = fanin_ids
+        self.expect = expect_count
+        self.received = {}
+        self.scape = scape
+        self.scape_inbox = asyncio.Queue()
+
+    async def run(self):
+
+        while True:
+            msg = await self.inbox.get()
+            tag = msg[0]
+
+            if tag == "forward":
+                _, from_id, vec = msg
+                self.received[from_id] = vec
+
+                if len(self.received) == self.expect:
+                    print("ACTUATOR: collected all signals...")
+                    output = []
+                    for fid in self.fanin_ids:
+                        output.extend(self.received[fid])
+
+                    if self.aname == "pts":
+                        print(f"Actuator output: {output}")
+                        fitness, halt_flag = 1.0, 0
+                    elif self.aname == "xor_SendOutput" and self.scape:
+                        print("ACTUATOR: sending action to scape...")
+                        await self.scape.send(("action", output, self))
+                        while True:
+                            resp = await self.inbox.get()
+                            if resp[0] == "result":
+                                print("ACTUATOR: got scape response: ", resp)
+                                fitness, halt_flag = resp[1], resp[2]
+                                break
+                    else:
+                        fitness, halt_flag = 0.0, 0
+
+                    await self.cx_pid.send(("sync", self.aid, fitness, halt_flag))
+                    print("ACTUATOR: sent sync message to cortex.")
+                    self.received.clear()
+
+            elif tag == "terminate":
+                return

experiments/stochastic_hillclimber/actors/best.json

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+{
+  "cortex": {
+    "id": 0.5107239887106383,
+    "sensor_ids": [
+      5.685637947817566e-10
+    ],
+    "actuator_ids": [
+      5.685637947817563e-10
+    ],
+    "neuron_ids": [
+      0.3776999353275148,
+      0.7030052650887313,
+      0.9936497204289092
+    ]
+  },
+  "sensor": {
+    "id": 5.685637947817566e-10,
+    "name": "xor_GetInput",
+    "vector_length": 2,
+    "cx_id": 0.5107239887106383,
+    "fanout_ids": [
+      0.3776999353275148,
+      0.7030052650887313
+    ]
+  },
+  "actuator": {
+    "id": 5.685637947817563e-10,
+    "name": "xor_SendOutput",
+    "vector_length": 1,
+    "cx_id": 0.5107239887106383,
+    "fanin_ids": [
+      0.9936497204289092
+    ]
+  },
+  "neurons": [
+    {
+      "id": 0.3776999353275148,
+      "layer_index": 0,
+      "cx_id": 0.5107239887106383,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 5.685637947817566e-10,
+          "weights": [
+            -1.7328567115118854,
+            0.31546591460152307
+          ]
+        }
+      ],
+      "output_ids": [
+        0.24149710385676537
+      ]
+    },
+    {
+      "id": 0.7030052650887313,
+      "layer_index": 0,
+      "cx_id": 0.5107239887106383,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 5.685637947817566e-10,
+          "weights": [
+            1.507492385500833,
+            -1.5181033637128052
+          ]
+        }
+      ],
+      "output_ids": [
+        0.24149710385676537
+      ]
+    },
+    {
+      "id": 0.9936497204289092,
+      "layer_index": 1,
+      "cx_id": 0.5107239887106383,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 0.3776999353275148,
+          "weights": [
+            0.9998252528454215
+          ]
+        },
+        {
+          "input_id": 0.7030052650887313,
+          "weights": [
+            -1.7243886895741118
+          ]
+        }
+      ],
+      "output_ids": [
+        5.685637947817563e-10
+      ]
+    }
+  ]
+}

experiments/stochastic_hillclimber/actors/cortex.py

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+# actors/cortex.py
+import time
+from actor import Actor
+
+
+class Cortex(Actor):
+    def __init__(self, cid, exoself_pid, sensor_pids, neuron_pids, actuator_pids):
+        super().__init__(f"Cortex-{cid}")
+        self.cid = cid
+        self.sensors = sensor_pids
+        self.neurons = neuron_pids
+        self.actuators = actuator_pids
+        self.exoself_pid = exoself_pid
+
+        self.awaiting_sync = set()  # AIDs, von denen wir im aktuellen Schritt noch Sync erwarten
+        self.fitness_acc = 0.0  # akkumulierte Fitness über die Episode
+        self.ef_acc = 0  # akkumulierte EndFlags im aktuellen Schritt/Episode
+        self.cycle_acc = 0  # Anzahl abgeschlossener Sense-Think-Act Zyklen
+        self.active = False  # aktiv/inaktiv (wartet auf reactivate)
+        self._t0 = None  # Startzeit der Episode
+
+    async def _kick_sensors(self):
+        for s in self.sensors:
+            await s.send(("sync",))
+
+    def _reset_for_new_cycle(self):
+        self.awaiting_sync = set(a.aid for a in self.actuators)
+
+    def _reset_for_new_episode(self):
+        self.fitness_acc = 0.0
+        self.ef_acc = 0
+        self.cycle_acc = 0
+        self._reset_for_new_cycle()
+        self._t0 = time.perf_counter()
+        self.active = True
+
+    async def run(self):
+        # Initialisierung: falls Actuatoren schon gesetzt sind, Episode starten
+        if self.actuators:
+            self._reset_for_new_episode()
+            await self._kick_sensors()
+
+        while True:
+            msg = await self.inbox.get()
+            tag = msg[0]
+
+            # Optional: Exoself kann AIDs registrieren, bevor wir starten
+            if tag == "register_actuators":
+                _, aids = msg
+                # Map aids auf echte Actuatoren falls nötig; hier übernehmen wir sie direkt
+                self.awaiting_sync = set(aids)
+                if not self.active:
+                    self._reset_for_new_episode()
+                    await self._kick_sensors()
+                continue
+
+            if tag == "sync" and self.active:
+                print("CORTEX: got sync message: ", msg)
+                # Aktuator meldet Schrittabschluss
+                _t, aid, fitness, halt_flag = msg
+
+                print("----------------")
+                print("_t:", _t)
+                print("aid:", aid)
+                print("fitness:", fitness)
+                print("halt_flag:", halt_flag)
+                print("----------------")
+
+                # akkumulieren
+                self.fitness_acc += float(fitness)
+                self.ef_acc += int(halt_flag)
+
+                # diesen Aktuator als "eingetroffen" markieren
+                if aid in self.awaiting_sync:
+                    self.awaiting_sync.remove(aid)
+
+                print("CORTEX: awaiting sync: ", self.awaiting_sync)
+
+                # Wenn alle Aktuatoren gemeldet haben, ist ein Zyklus fertig
+                if not self.awaiting_sync:
+                    print("CORTEX: cycle completed.")
+                    self.cycle_acc += 1
+
+                    # Episodenende, wenn irgendein Aktuator EndFlag setzt
+                    if self.ef_acc > 0:
+                        elapsed = time.perf_counter() - self._t0
+                        # An Exoself berichten
+                        await self.exoself_pid.send((
+                            "evaluation_completed",
+                            self.fitness_acc,
+                            self.cycle_acc,
+                            elapsed
+                        ))
+                        # inaktiv werden – warten auf reactivate
+                        self.active = False
+                        # Flags für nächste Episode werden erst bei reactivate gesetzt
+                    else:
+                        # Nächsten Zyklus starten
+                        self.ef_acc = 0
+                        self._reset_for_new_cycle()
+                        await self._kick_sensors()
+
+                continue
+
+            if tag == "reactivate":
+                # neue Episode starten
+                self._reset_for_new_episode()
+                await self._kick_sensors()
+                continue
+
+            if tag == "terminate":
+                # geordnet alle Kinder beenden
+                for a in (self.sensors + self.actuators + self.neurons):
+                    await a.send(("terminate",))
+                return
+
+            elif tag == "backup_from_neuron":
+                # vom Neuron an Cortex -> an Exoself weiterreichen
+                await self.exoself_pid.send(msg)

experiments/stochastic_hillclimber/actors/exoself.py

@@ -0,0 +1,346 @@
+# exoself.py
+import asyncio
+import json
+import math
+import random
+from collections import defaultdict
+from typing import Any, Dict, List, Tuple, Optional
+
+from actor import Actor
+from cortex import Cortex
+from sensor import Sensor
+from neuron import Neuron
+from actuator import Actuator
+from scape import XorScape
+
+
+class Exoself(Actor):
+    """
+    Exoself übersetzt den Genotyp (JSON) in einen laufenden Phenotyp (Actors) und
+    steuert das simple Neuroevolution-Training (Backup/Restore/Perturb + Reactivate).
+    """
+    def __init__(self, genotype: Dict[str, Any], file_name: Optional[str] = None):
+        super().__init__("Exoself")
+        self.g = genotype
+        self.file_name = file_name
+
+        self.cx_actor: Optional[Cortex] = None
+        self.sensor_actors: List[Sensor] = []
+        self.neuron_actors: List[Neuron] = []
+        self.actuator_actors: List[Actuator] = []
+
+        self.tasks: List[asyncio.Task] = []
+
+        # Training-Stats
+        self.highest_fitness = float("-inf")
+        self.eval_acc = 0
+        self.cycle_acc = 0
+        self.time_acc = 0.0
+        self.attempt = 0
+        self.MAX_ATTEMPTS = 50
+        self.actuator_scape = None
+
+        # zuletzt perturbierte Neuronen (für Restore)
+        self._perturbed: List[Neuron] = []
+
+    # ---------- Convenience ----------
+    @staticmethod
+    def from_file(path: str) -> "Exoself":
+        with open(path, "r") as f:
+            g = json.load(f)
+        return Exoself(g, file_name=path)
+
+    # ---------- Public API ----------
+    async def run(self):
+        # 1) Netzwerk bauen
+        self._build_pid_map_and_spawn()
+
+        # 2) Cortex verlinken + starten
+        self._link_cortex()
+
+        # 3) Actors starten (Sensoren/Neuronen/Aktuatoren)
+        for a in self.sensor_actors + self.neuron_actors + self.actuator_actors + [self.actuator_scape]:
+            self.tasks.append(asyncio.create_task(a.run()))
+
+        # 4) Hauptloop: auf Cortex-Events hören
+        while True:
+            msg = await self.inbox.get()
+            tag = msg[0]
+
+            if tag == "evaluation_completed":
+                _, fitness, cycles, elapsed = msg
+                await self._on_evaluation_completed(fitness, cycles, elapsed)
+
+            elif tag == "terminate":
+                await self._terminate_all()
+                return
+
+        # in exoself.py, innerhalb der Klasse Exoself
+
+    async def run_evaluation(self):
+        """
+        Eine einzelne Episode/Evaluation:
+        - baut & verlinkt das Netz
+        - startet alle Actors
+        - wartet auf 'evaluation_completed' vom Cortex
+        - beendet alles und liefert (fitness, evals, cycles, elapsed)
+        """
+        # 1) Netzwerk bauen & Cortex verlinken
+        print("build network and link...")
+        self._build_pid_map_and_spawn()
+        print("link cortex...")
+        self._link_cortex()
+
+        # 2) Sensor/Neuron/Aktuator-Tasks starten (Cortex startete _link_cortex bereits)
+        for a in self.sensor_actors + self.neuron_actors + self.actuator_actors:
+            self.tasks.append(asyncio.create_task(a.run()))
+
+        if self.actuator_scape:
+            self.tasks.append(asyncio.create_task(self.actuator_scape.run()))
+
+        print("network actors are running...")
+
+        # 3) Auf Abschluss warten
+        while True:
+            msg = await self.inbox.get()
+            print("message in exsoself: ", msg)
+            tag = msg[0]
+            if tag == "evaluation_completed":
+                _, fitness, cycles, elapsed = msg
+                # 4) Sauber terminieren
+                await self._terminate_all()
+                # Evals = 1 (eine Episode)
+                return float(fitness), 1, int(cycles), float(elapsed)
+            elif tag == "terminate":
+                await self._terminate_all()
+                # Falls vorzeitig terminiert wurde
+                return float("-inf"), 0, 0, 0.0
+
+    # ---------- Build ----------
+    def _build_pid_map_and_spawn(self):
+        """
+        Baut Cortex, dann alle Neuronen (mit cx_pid=self.cx_actor), dann verlinkt Outputs schichtweise,
+        dann Sensoren/Aktuatoren (mit cx_pid=self.cx_actor). Achtung: Reihenfolge wichtig.
+        """
+        cx = self.g["cortex"]
+        # Cortex zuerst (damit wir cx_pid an Kinder übergeben können)
+        self.cx_actor = Cortex(
+            cid=cx["id"],
+            exoself_pid=self,
+            sensor_pids=[],   # werden gleich gesetzt
+            neuron_pids=[],
+            actuator_pids=[]
+        )
+
+        self.actuator_scape = XorScape()
+
+        # Neuronen nach Layer gruppieren (damit outputs korrekt gesetzt werden)
+        layers: Dict[int, List[Dict[str, Any]]] = defaultdict(list)
+        for n in self.g["neurons"]:
+            layers[n["layer_index"]].append(n)
+        ordered_layers = [layers[i] for i in sorted(layers)]
+
+        # Platzhalter: wir benötigen später Referenzen nach ID
+        id2neuron_actor: Dict[Any, Neuron] = {}
+
+        # Zuerst alle Neuronen erzeugen (ohne Outputs), damit wir Referenzen haben
+        for layer in ordered_layers:
+            for n in layer:
+                input_idps = [(iw["input_id"], iw["weights"]) for iw in n["input_weights"]]
+                neuron = Neuron(
+                    nid=n["id"],
+                    cx_pid=self.cx_actor,
+                    af_name=n.get("activation_function", "tanh"),
+                    input_idps=input_idps,
+                    output_pids=[]  # füllen wir gleich
+                )
+                id2neuron_actor[n["id"]] = neuron
+                self.neuron_actors.append(neuron)
+
+        # Jetzt Outputs pro Layer setzen:
+        # - für Nicht-Output-Layer: Outputs = Neuronen der nächsten Schicht
+        # - für Output-Layer: Outputs = Aktuator(en) (setzen wir nachdem Aktuatoren erzeugt sind)
+        for li in range(len(ordered_layers) - 1):
+            next_pids = [id2neuron_actor[nx["id"]] for nx in ordered_layers[li + 1]]
+            for n in ordered_layers[li]:
+                id2neuron_actor[n["id"]].outputs = next_pids
+
+        # Aktuatoren anlegen (brauchen cx_pid und fanin_ids)
+        # Genotyp kann "actuator" (ein Objekt) oder "actuators" (Liste) haben – wir unterstützen beides.
+        actuators = self._get_actuators_block()
+        if not actuators:
+            raise ValueError("Genotype must include 'actuator' or 'actuators'.")
+
+        for a in actuators:
+            fanin_ids = a.get("fanin_ids", [])
+            expect = len(fanin_ids) if fanin_ids else 0
+            actuator = Actuator(
+                aid=a["id"],
+                cx_pid=self.cx_actor,
+                name=a["name"],
+                fanin_ids=fanin_ids,
+                expect_count=expect,
+                scape=self.actuator_scape
+            )
+            self.actuator_actors.append(actuator)
+
+        # Output-Layer Neuronen → Outputs = Aktuatoren
+        if ordered_layers:
+            last_layer = ordered_layers[-1]
+            out_targets = self.actuator_actors  # Liste
+            for n in last_layer:
+                id2neuron_actor[n["id"]].outputs = out_targets
+
+        # Sensor(en) anlegen (brauchen cx_pid und fanout auf erste Schicht)
+        sensors = self._get_sensors_block()
+        if not sensors:
+            raise ValueError("Genotype must include 'sensor' or 'sensors'.")
+
+        first_layer = ordered_layers[0] if ordered_layers else []
+        first_layer_pids = [id2neuron_actor[n["id"]] for n in first_layer]
+
+        for s in sensors:
+            sensor = Sensor(
+                sid=s["id"],
+                cx_pid=self.cx_actor,
+                name=s["name"],
+                vector_length=s["vector_length"],
+                fanout_pids=first_layer_pids,
+                scape=self.actuator_scape
+            )
+            self.sensor_actors.append(sensor)
+
+    def _get_sensors_block(self) -> List[Dict[str, Any]]:
+        if "sensors" in self.g:
+            return list(self.g["sensors"])
+        if "sensor" in self.g:
+            return [self.g["sensor"]]
+        return []
+
+    def _get_actuators_block(self) -> List[Dict[str, Any]]:
+        if "actuators" in self.g:
+            return list(self.g["actuators"])
+        if "actuator" in self.g:
+            return [self.g["actuator"]]
+        return []
+
+    # ---------- Link ----------
+    def _link_cortex(self):
+        """
+        Übergibt dem Cortex die fertigen Listen und setzt awaiting_sync.
+        Startet dann den Cortex-Task.
+        """
+        self.cx_actor.sensors = [a for a in self.sensor_actors if a]
+        self.cx_actor.neurons = [a for a in self.neuron_actors if a]
+        self.cx_actor.actuators = [a for a in self.actuator_actors if a]
+
+        # Wichtig: vor Start die erwarteten AIDs setzen,
+        # damit der erste Sensor-Trigger nicht in eine leere awaiting_sync läuft.
+        self.cx_actor.awaiting_sync = set(a.aid for a in self.cx_actor.actuators)
+
+        # Cortex starten
+        self.tasks.append(asyncio.create_task(self.cx_actor.run()))
+
+    # ---------- Training-Loop Reaction ----------
+    async def _on_evaluation_completed(self, fitness: float, cycles: int, elapsed: float):
+        self.eval_acc += 1
+        self.cycle_acc += int(cycles)
+        self.time_acc += float(elapsed)
+
+        print(f"[Exoself] evaluation_completed: fitness={fitness:.6f} cycles={cycles} time={elapsed:.3f}s")
+
+        if fitness > self.highest_fitness:
+            self.highest_fitness = fitness
+            self.attempt = 0
+            # Backup aller Neuronen
+            for n in self.neuron_actors:
+                await n.send(("weight_backup",))
+        else:
+            self.attempt += 1
+            # Restore nur der zuletzt perturbierten Neuronen
+            for n in self._perturbed:
+                await n.send(("weight_restore",))
+
+        # Stop-Kriterium?
+        if self.attempt >= self.MAX_ATTEMPTS:
+            print(f"[Exoself] STOP. Best fitness={self.highest_fitness:.6f} evals={self.eval_acc} cycles={self.cycle_acc}")
+            await self._backup_genotype()
+            await self._terminate_all()
+            return {
+                "best_fitness": self.highest_fitness,
+                "eval_acc": self.eval_acc,
+                "cycle_acc": self.cycle_acc,
+                "time_acc": self.time_acc,
+            }
+
+        # Perturbiere Teilmenge der Neuronen
+        tot = len(self.neuron_actors)
+        mp = 1.0 / math.sqrt(max(1, tot))
+        self._perturbed = [n for n in self.neuron_actors if random.random() < mp]
+
+        for n in self._perturbed:
+            await n.send(("weight_perturb",))
+
+        # Nächste Episode starten
+        await self.cx_actor.send(("reactivate",))
+
+    # ---------- Backup Genotype ----------
+    async def _backup_genotype(self):
+        """
+        Holt von allen Neuronen die aktuellen Weights und schreibt sie in self.g.
+        Speichert optional in self.file_name.
+        """
+        # 1) Request
+        remaining = len(self.neuron_actors)
+        for n in self.neuron_actors:
+            await n.send(("get_backup",))
+
+        # 2) Collect vom Cortex-Postfach (Neuronen senden an cx_pid → cx leitet an Exoself weiter
+        # oder du hast sie direkt an Exoself schicken lassen; falls direkt an Cortex, dann
+        # lausche hier stattdessen auf self.cx_actor.inbox. In deinem Neuron-Code geht es an cx_pid,
+        # und in deiner bisherigen Implementierung hast du aus dem Cortex-Postfach gelesen.
+        # Hier vereinfachen wir: Neuronen senden direkt an EXOSELF (passe Neuron ggf. an).
+        backups: List[Tuple[Any, List[Tuple[Any, List[float]]]]] = []
+
+        while remaining > 0:
+            msg = await self.inbox.get()
+            if msg[0] == "backup_from_neuron":
+                _, nid, idps = msg
+                backups.append((nid, idps))
+                remaining -= 1
+
+        # 3) Update JSON
+        # exoself.py -> in _backup_genotype()
+        id2n = {n["id"]: n for n in self.g["neurons"]}
+        for nid, idps in backups:
+            if nid not in id2n:
+                continue
+            new_iw = []
+            bias_val = None
+            for item in idps:
+                if isinstance(item[0], str) and item[0] == "bias":
+                    bias_val = float(item[1]) if not isinstance(item[1], list) else float(item[1][0])
+                else:
+                    input_id, weights = item
+                    new_iw.append({"input_id": input_id, "weights": list(weights)})
+            id2n[nid]["input_weights"] = new_iw
+            # Bias mit abspeichern (Variante B):
+            if bias_val is not None:
+                id2n[nid].setdefault("input_weights", []).append({"input_id": "bias", "weights": [bias_val]})
+
+        # 4) Save
+        if self.file_name:
+            with open(self.file_name, "w") as f:
+                json.dump(self.g, f, indent=2)
+            print(f"[Exoself] Genotype updated → {self.file_name}")
+
+    # ---------- Termination ----------
+    async def _terminate_all(self):
+        for a in self.sensor_actors + self.neuron_actors + self.actuator_actors:
+            await a.send(("terminate",))
+        if self.cx_actor:
+            await self.cx_actor.send(("terminate",))
+        for t in self.tasks:
+            if not t.done():
+                t.cancel()
+        self.tasks.clear()

experiments/stochastic_hillclimber/actors/experimental.json

@@ -0,0 +1,96 @@
+{
+  "cortex": {
+    "id": 0.0873421806271496,
+    "sensor_ids": [
+      5.6856379409509e-10
+    ],
+    "actuator_ids": [
+      5.685637940950896e-10
+    ],
+    "neuron_ids": [
+      0.6384794610574114,
+      0.6023131059017351,
+      0.39277072802910173
+    ]
+  },
+  "sensor": {
+    "id": 5.6856379409509e-10,
+    "name": "xor_GetInput",
+    "vector_length": 2,
+    "cx_id": 0.0873421806271496,
+    "fanout_ids": [
+      0.6384794610574114,
+      0.6023131059017351
+    ]
+  },
+  "actuator": {
+    "id": 5.685637940950896e-10,
+    "name": "xor_SendOutput",
+    "vector_length": 1,
+    "cx_id": 0.0873421806271496,
+    "fanin_ids": [
+      0.39277072802910173
+    ]
+  },
+  "neurons": [
+    {
+      "id": 0.6384794610574114,
+      "layer_index": 0,
+      "cx_id": 0.0873421806271496,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 5.6856379409509e-10,
+          "weights": [
+            -1.241701053140722,
+            -0.9643293453192259
+          ]
+        }
+      ],
+      "output_ids": [
+        0.20086494757397733
+      ]
+    },
+    {
+      "id": 0.6023131059017351,
+      "layer_index": 0,
+      "cx_id": 0.0873421806271496,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 5.6856379409509e-10,
+          "weights": [
+            -0.38795737506820904,
+            -0.5125123920689245
+          ]
+        }
+      ],
+      "output_ids": [
+        0.20086494757397733
+      ]
+    },
+    {
+      "id": 0.39277072802910173,
+      "layer_index": 1,
+      "cx_id": 0.0873421806271496,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 0.6384794610574114,
+          "weights": [
+            -0.7426574958298033
+          ]
+        },
+        {
+          "input_id": 0.6023131059017351,
+          "weights": [
+            -0.9823211499227558
+          ]
+        }
+      ],
+      "output_ids": [
+        5.685637940950896e-10
+      ]
+    }
+  ]
+}

experiments/stochastic_hillclimber/actors/genotype.py

@@ -0,0 +1,266 @@
+# genotype.py
+import json
+import math
+import random
+import time
+from typing import Dict, List, Tuple, Any, Optional
+
+
+# -----------------------------
+# Utilities / ID & Weights
+# -----------------------------
+def generate_id() -> float:
+    return random.random()
+
+
+def create_neural_weights(vector_length: int) -> List[float]:
+    return [random.uniform(-2.0, 2.0) for _ in range(vector_length)]
+
+
+# -----------------------------
+# Morphology "Interface"
+# -----------------------------
+"""
+Erwartete Morphologie-API (duck-typed):
+
+- get_InitSensor(morphology) -> dict mit Feldern:
+    {
+        "id": <beliebig>,
+        "name": <sensor-funktionsname, z.B. "rng" oder "xor_GetInput">,
+        "vector_length": <int>,
+        # optional: "scape": <frei nutzbar>,
+    }
+
+- get_InitActuator(morphology) -> dict mit Feldern:
+    {
+        "id": <beliebig>,
+        "name": <aktor-funktionsname, z.B. "pts" oder "xor_SendOutput">,
+        "vector_length": <int>,
+        # optional: "scape": <frei nutzbar>,
+    }
+
+Du kannst z.B. ein kleines Modul/Objekt übergeben, das diese Funktionen bereitstellt.
+"""
+
+
+# -----------------------------
+# Genotype-Erzeugung
+# -----------------------------
+def construct(
+    morphology_module,
+    hidden_layer_densities: List[int],
+    file_name: Optional[str] = None,
+    *,
+    add_bias: bool = False,
+) -> Dict[str, Any]:
+    """
+    Baut einen Genotyp (JSON-kompatibles Dict) analog zur Erlang-Version:
+
+    - wählt Initial-Sensor & -Aktuator aus Morphologie
+    - bestimmt Output-Layer-Dichte = actuator.vector_length
+    - erzeugt alle Neuronen-Schichten & Gewichte
+    - setzt fanout_ids/fanin_ids
+    - erstellt Cortex mit Listen der IDs
+    - speichert optional in file_name
+
+    add_bias: Wenn True, hängt pro Neuron einen Bias als ('bias', b) an die Input-Liste an.
+              Dein bisheriges JSON nutzt KEIN Bias-Element. Standard=False für Kompatibilität.
+    """
+
+    # Seed wie im Buch
+    rnd_seed = time.time_ns() & 0xFFFFFFFF
+    random.seed(rnd_seed)
+
+    # 1) Sensor & Aktuator aus Morphologie
+    S = morphology_module.get_InitSensor(morphology_module)
+    A = morphology_module.get_InitActuator(morphology_module)
+
+    # Pflichtfelder normalisieren
+    sensor = {
+        "id": S.get("id", generate_id()),
+        "name": S["name"],
+        "vector_length": int(S["vector_length"]),
+        "cx_id": None,            # wird später gesetzt
+        "fanout_ids": [],         # wird später gesetzt
+        # optional:
+        # "scape": S.get("scape")
+    }
+
+    actuator = {
+        "id": A.get("id", generate_id()),
+        "name": A["name"],
+        "vector_length": int(A["vector_length"]),
+        "cx_id": None,            # wird später gesetzt
+        "fanin_ids": [],          # wird später gesetzt
+        # optional:
+        # "scape": A.get("scape")
+    }
+
+    # 2) Output-Layer-Dichte = actuator.vector_length (wie im Buch)
+    output_vl = actuator["vector_length"]
+    layer_densities = list(hidden_layer_densities) + [output_vl]
+
+    # 3) Cortex-ID festlegen
+    cortex_id = generate_id()
+
+    # 4) Neuronen-Schichten erzeugen
+    layers = _create_neuro_layers(
+        cx_id=cortex_id,
+        sensor=sensor,
+        actuator=actuator,
+        layer_densities=layer_densities,
+        add_bias=add_bias,
+    )
+
+    # 5) Sensor/Aktuator mit Cortex/Fanout/Fanin verbinden
+    input_layer = layers[0]
+    output_layer = layers[-1]
+
+    sensor["cx_id"] = cortex_id
+    sensor["fanout_ids"] = [n["id"] for n in input_layer]
+
+    actuator["cx_id"] = cortex_id
+    actuator["fanin_ids"] = [n["id"] for n in output_layer]
+
+    # 6) Cortex-Objekt
+    neuron_ids = [n["id"] for layer in layers for n in layer]
+    cortex = {
+        "id": cortex_id,
+        "sensor_ids": [sensor["id"]],
+        "actuator_ids": [actuator["id"]],
+        "neuron_ids": neuron_ids,
+    }
+
+    # 7) Genotyp zusammensetzen
+    genotype = {
+        "cortex": cortex,
+        "sensor": sensor,
+        "actuator": actuator,
+        "neurons": [n for layer in layers for n in layer],
+    }
+
+    # 8) Optional speichern
+    if file_name:
+        save_genotype(file_name, genotype)
+
+    return genotype
+
+
+def _create_neuro_layers(
+    cx_id: float,
+    sensor: Dict[str, Any],
+    actuator: Dict[str, Any],
+    layer_densities: List[int],
+    *,
+    add_bias: bool,
+) -> List[List[Dict[str, Any]]]:
+    """
+    Baut alle Neuronen-Schichten.
+    - Erste Schicht erhält als Input den Sensor (Vektor).
+    - Mittlere Schichten erhalten skalare Inputs von voriger Schicht.
+    - Letzte Schicht verbindet zum Aktuator (output_ids = [actuator.id]).
+    """
+    layers: List[List[Dict[str, Any]]] = []
+
+    # Platzhalter-Input: (input_id, vector_length)
+    input_idps: List[Tuple[float, int]] = [(sensor["id"], sensor["vector_length"])]
+
+    for layer_index, layer_density in enumerate(layer_densities):
+        # Neuron-IDs generieren
+        neuron_ids = [generate_id() for _ in range(layer_density)]
+
+        # output_ids: Standardmäßig IDs der nächsten Schicht (werden nach dem Layer bekannt)
+        if layer_index < len(layer_densities) - 1:
+            # IDs der nächste Schicht vorab erzeugen (für output_ids)
+            next_ids = [generate_id() for _ in range(layer_densities[layer_index + 1])]
+            # Aber: wir erzeugen hier *nur* die IDs für output_ids, nicht die Neuronen der nächsten Schicht
+            output_ids = next_ids
+        else:
+            # letzte Schicht → zum Aktuator
+            output_ids = [actuator["id"]]
+
+        # Layer-Neuronen erzeugen
+        this_layer: List[Dict[str, Any]] = []
+        for _nid in neuron_ids:
+            proper_input = _create_neural_input(input_idps, add_bias=add_bias)
+            neuron = {
+                "id": _nid,
+                "layer_index": layer_index,
+                "cx_id": cx_id,
+                "activation_function": "tanh",
+                # JSON-Konvention: [{"input_id": id, "weights": [...]}, ...]
+                "input_weights": [{"input_id": i, "weights": w} for (i, w) in proper_input if not _is_bias_tuple((i, w))],
+                "output_ids": output_ids[:],  # Kopie
+            }
+            this_layer.append(neuron)
+
+        layers.append(this_layer)
+
+        # Inputs für nächste Schicht: jetzt sind es skalare Outputs der aktuellen Neuronen
+        input_idps = [(n["id"], 1) for n in this_layer]
+
+    # WICHTIG: oben haben wir für mittlere Layer „künstliche“ next_ids erzeugt, damit output_ids befüllt waren.
+    # In deiner Laufzeit-Topologie ignorieren wir diese Platzhalter und verdrahten später im Exoself die
+    # tatsächlichen Actor-Referenzen schichtweise. Für den Genotyp sind die output_ids der Zwischenlayer
+    # nicht kritisch (du überschreibst sie ohnehin beim Phenotype-Mapping). Die letzte Schicht zeigt korrekt auf den Aktuator.
+
+    return layers
+
+
+def _is_bias_tuple(t: Tuple[Any, Any]) -> bool:
+    """Hilfsfunktion falls du später Bias im JSON aufnehmen willst."""
+    key, _ = t
+    return isinstance(key, str) and key == "bias"
+
+
+def _create_neural_input(
+    input_idps: List[Tuple[float, int]],
+    *,
+    add_bias: bool,
+) -> List[Tuple[Any, List[float]]]:
+    """
+    Wandelt Platzhalter (input_id, vector_length) in (input_id, weights[]) um.
+    Optional einen Bias als ('bias', [b]) anhängen (Erlang-Variante).
+    """
+    proper: List[Tuple[Any, List[float]]] = []
+    for input_id, vl in input_idps:
+        proper.append((input_id, create_neural_weights(vl)))
+
+    if add_bias:
+        proper.append(("bias", [random.random() - 0.5]))
+
+    return proper
+
+
+# -----------------------------
+# File I/O & Print
+# -----------------------------
+def save_genotype(file_name: str, genotype: Dict[str, Any]) -> None:
+    with open(file_name, "w") as f:
+        json.dump(genotype, f, indent=2)
+
+
+def load_from_file(file_name: str) -> Dict[str, Any]:
+    with open(file_name, "r") as f:
+        return json.load(f)
+
+
+def print_genotype(file_name: str) -> None:
+    g = load_from_file(file_name)
+    cx = g["cortex"]
+    print("[CORTEX]", cx)
+    sids = cx.get("sensor_ids", [])
+    nids = cx.get("neuron_ids", [])
+    aids = cx.get("actuator_ids", [])
+
+    # Indexe für schnellen Zugriff
+    nid2n = {n["id"]: n for n in g.get("neurons", [])}
+    sid2s = {g["sensor"]["id"]: g["sensor"]} if "sensor" in g else {s["id"]: s for s in g.get("sensors", [])}
+    aid2a = {g["actuator"]["id"]: g["actuator"]} if "actuator" in g else {a["id"]: a for a in g.get("actuators", [])}
+
+    for sid in sids:
+        print("[SENSOR]", sid2s.get(sid))
+    for nid in nids:
+        print("[NEURON]", nid2n.get(nid))
+    for aid in aids:
+        print("[ACTUATOR]", aid2a.get(aid))

experiments/stochastic_hillclimber/actors/morphology.py

@@ -0,0 +1,104 @@
+# morphology.py
+import time
+from typing import Any, Callable, Dict, List, Union
+
+MorphologyType = Union[str, Callable[[str], List[Dict[str, Any]]]]
+
+
+def generate_id() -> float:
+    """
+    Zeitbasierte float-ID (ähnlich wie im Buch).
+    """
+    now = time.time()
+    return 1.0 / now
+
+
+# ------------------------------------------------------------
+# Morphologie-API (duck-typed, wie im Erlang-Original)
+# ------------------------------------------------------------
+def get_InitSensor(morphology: MorphologyType) -> Dict[str, Any]:
+    sensors = get_Sensors(morphology)
+    if not sensors:
+        raise ValueError("Morphology has no sensors.")
+    return sensors[0]
+
+
+def get_InitActuator(morphology: MorphologyType) -> Dict[str, Any]:
+    actuators = get_Actuators(morphology)
+    if not actuators:
+        raise ValueError("Morphology has no actuators.")
+    return actuators[0]
+
+
+def get_Sensors(morphology: MorphologyType) -> List[Dict[str, Any]]:
+    fn = _resolve_morphology(morphology)
+    return fn("sensors")
+
+
+def get_Actuators(morphology: MorphologyType) -> List[Dict[str, Any]]:
+    fn = _resolve_morphology(morphology)
+    return fn("actuators")
+
+
+def _resolve_morphology(morphology: MorphologyType) -> Callable[[str], List[Dict[str, Any]]]:
+    """
+    Akzeptiert:
+      - eine Callable Morphologie-Funktion (z.B. xor_mimic)
+      - einen String (z.B. "xor_mimic") -> per Registry auflösen
+      - ein Modul-Objekt (z.B. import morphology) -> Standard 'xor_mimic' aus dem Modul
+    """
+    # 1) Bereits eine Callable-Funktion? (z.B. xor_mimic)
+    if callable(morphology):
+        return morphology
+
+    # 2) String -> Registry
+    if isinstance(morphology, str):
+        reg = {
+            "xor_mimic": xor_mimic,
+            # weitere Morphologien hier registrieren...
+        }
+        if morphology in reg:
+            return reg[morphology]
+        raise ValueError(f"Unknown morphology name: {morphology}")
+
+    # 3) Modul-Objekt: versuche eine Standard-Morphologie-Funktion daraus zu nehmen
+    #    Hier nehmen wir 'xor_mimic' als Default (du kannst das generalisieren).
+    try:
+        # Ist es ein Modul mit einer Funktion 'xor_mimic'?
+        if hasattr(morphology, "xor_mimic") and callable(getattr(morphology, "xor_mimic")):
+            return getattr(morphology, "xor_mimic")
+    except Exception:
+        pass
+
+    raise TypeError("morphology must be a callable, a module with 'xor_mimic', or a registered string key")
+
+
+# ------------------------------------------------------------
+# Beispiel-Morphologie: XOR (wie im Buch)
+# ------------------------------------------------------------
+def xor_mimic(kind: str) -> List[Dict[str, Any]]:
+    """
+    Liefert je nach 'kind' ('sensors' | 'actuators') die Definitionen.
+    Felder sind so benannt, dass sie direkt mit unserem Genotype/Phenotype-Code
+    kompatibel sind (vector_length statt 'vl', etc.).
+    """
+    if kind == "sensors":
+        return [
+            {
+                "id": generate_id(),
+                "name": "xor_GetInput",       # Sensorfunktion (muss in deinem Sensor-Actor implementiert sein)
+                "vector_length": 2,
+                "scape": {"private": "xor_sim"}  # optional, falls du Scapes nutzt
+            }
+        ]
+    elif kind == "actuators":
+        return [
+            {
+                "id": generate_id(),
+                "name": "xor_SendOutput",     # Aktuatorfunktion (muss in deinem Actuator-Actor implementiert sein)
+                "vector_length": 1,
+                "scape": {"private": "xor_sim"}  # optional
+            }
+        ]
+    else:
+        raise ValueError(f"xor_mimic: unsupported kind '{kind}', expected 'sensors' or 'actuators'")

experiments/stochastic_hillclimber/actors/mtest.py

@@ -0,0 +1,39 @@
+import asyncio
+
+import morphology
+from experiments.stochastic_hillclimber.actors.trainer import Trainer
+from genotype import construct, save_genotype
+
+
+def test_genotype_construction():
+    """
+    genotype_data = construct(morphology, hidden_layer_densities=[3, 2])
+
+    # Prüfen, ob Cortex, Sensor, Actuator und Neuronen existieren
+    assert "cortex" in genotype_data
+    assert len(genotype_data["neurons"]) == 3 + 2 + 1  # 3 in 1. HL, 2 in 2. HL, 1 Output
+
+    print("Genotype construction OK")
+    print("Cortex:", genotype_data["cortex"])
+    print("---------------------------------")
+    print("Neurons:", genotype_data["neurons"])
+    print("---------------------------------")
+    print("Actuators:", genotype_data["actuator"])
+
+    save_genotype("test.json", genotype_data)
+    """
+
+    trainer = Trainer(
+        morphology_spec=morphology,  # <— wichtig! callable oder "xor_mimic"
+        hidden_layer_densities=[2],  # wie im Buchbeispiel
+        max_attempts=float("inf"),  # MA=inf
+        eval_limit=float("inf"),  # EL=inf
+        fitness_target=99.9,  # FT=99.9
+        experimental_file="experimental.json",
+        best_file="best.json",
+        exoself_steps_per_eval=0,  # 0 = Scape/Cortex entscheiden über Halt
+    )
+    asyncio.run(trainer.go())
+
+
+test_genotype_construction()

experiments/stochastic_hillclimber/actors/neuron.py

@@ -0,0 +1,105 @@
+# actors/neuron.py
+import math
+import random
+
+from actor import Actor
+
+
+def tanh(x): return math.tanh(x)
+
+
+class Neuron(Actor):
+    def __init__(self, nid, cx_pid, af_name, input_idps, output_pids):
+        super().__init__(f"Neuron-{nid}")
+        self.nid = nid
+        self.cx_pid = cx_pid
+        self.af = tanh if af_name == "tanh" else tanh
+        # input_idps: [(input_id, [w1, w2, ...])]
+        self.inputs = {}
+        self.order = []
+        for (inp_id, weights) in input_idps:
+            self.order.append(inp_id)
+            self.inputs[inp_id] = {"weights": list(weights), "got": False, "val": None}
+        # WICHTIG: Bias lernbar machen
+        self.bias = random.uniform(-2.0, 2.0)
+        # Für Backup/Restore
+        self._backup_inputs = None
+        self._backup_bias = None
+
+        self.outputs = output_pids
+
+        print(f"Neuron {nid}: inputs={list(self.inputs.keys())}, bias={self.bias}")
+
+    async def run(self):
+        while True:
+            msg = await self.inbox.get()
+            tag = msg[0]
+
+            if tag == "forward":
+                _, from_id, data = msg
+                if from_id not in self.inputs:
+                    continue
+                slot = self.inputs[from_id]
+                slot["got"] = True
+                slot["val"] = data
+
+                if all(self.inputs[i]["got"] for i in self.order):
+                    acc = 0.0
+                    for i in self.order:
+                        w = self.inputs[i]["weights"]
+                        v = self.inputs[i]["val"]
+                        if isinstance(v, list):
+                            acc += sum(wj * vj for wj, vj in zip(w, v))
+                        else:
+                            acc += w[0] * float(v)
+                    out = self.af(acc + self.bias)
+                    for pid in self.outputs:
+                        await pid.send(("forward", self.nid, [out]))
+                    for i in self.order:
+                        self.inputs[i]["got"] = False
+                        self.inputs[i]["val"] = None
+                    print(f"Neuron {self.nid}: input_sum={acc + self.bias:.3f}, output={out:.3f}")
+
+            elif tag == "get_backup":
+                # Packe Gewichte + Bias zurück
+                idps = [(i, self.inputs[i]["weights"]) for i in self.order]
+                idps.append(("bias", self.bias))
+                await self.cx_pid.send(("backup_from_neuron", self.nid, idps))
+
+            elif tag == "weight_backup":
+                # Tiefkopie der Gewichte + Bias
+                print(f"Neuron {self.nid}: backing up weights")
+                self._backup_inputs = {k: {"weights": v["weights"][:]} for k, v in self.inputs.items()}
+                self._backup_bias = self.bias
+
+            elif tag == "weight_restore":
+                if self._backup_inputs is not None:
+                    for k in self.inputs:
+                        self.inputs[k]["weights"] = self._backup_inputs[k]["weights"][:]
+                    self.bias = self._backup_bias
+
+            elif tag == "weight_perturb":
+                # In neuron.py weight_perturb, add:
+                print(f"Neuron {self.nid}: perturbing {len([w for i in self.order for w in self.inputs[i]['weights']])} weights")
+                # MP wie im Buch: 1/sqrt(TotalWeightsIncludingBias)
+                tot_w = sum(len(self.inputs[i]["weights"]) for i in self.order) + 1
+                mp = 1 / math.sqrt(tot_w)
+                delta_mag = 2.0 * math.pi
+                sat_lim = 2.0 * math.pi
+
+                # Gewichte perturbieren
+                for i in self.order:
+                    ws = self.inputs[i]["weights"]
+                    for j in range(len(ws)):
+                        if random.random() < mp:
+                            ws[j] = _sat(ws[j] + (random.random() - 0.5) * delta_mag, -sat_lim, sat_lim)
+                # Bias perturbieren
+                if random.random() < mp:
+                    self.bias = _sat(self.bias + (random.random() - 0.5) * delta_mag, -sat_lim, sat_lim)
+
+            elif tag == "terminate":
+                return
+
+
+def _sat(val, lo, hi):
+    return lo if val < lo else (hi if val > hi else val)

experiments/stochastic_hillclimber/actors/scape.py

@@ -0,0 +1,58 @@
+# actors/scape.py
+from actor import Actor
+import math
+
+
+class XorScape(Actor):
+    def __init__(self):
+        super().__init__("XorScape")
+        self.data = [
+            ([-1, -1], [-1]),
+            ([1, -1], [1]),
+            ([-1, 1], [1]),
+            ([1, 1], [-1])
+        ]
+        self.index = 0
+        self.error_acc = 0.0
+        self.last_actuator = None  # <-- wer uns zuletzt "action" geschickt hat
+
+    async def run(self):
+        while True:
+            msg = await self.inbox.get()
+            tag = msg[0]
+
+            if tag == "sense":
+                print("SCAPE: got sensed by sensor...")
+                # Sensor fragt nach Input
+                _, sid, from_pid = msg
+                self.last_actuator = from_pid  # merken, wer beteiligt ist
+                inputs, correct = self.data[self.index]
+                print("SENSOR input /correct: ", inputs, correct)
+                await from_pid.send(("percept", inputs))
+
+            elif tag == "action":
+                _, output, from_pid = msg
+                _, correct = self.data[self.index]
+
+                # Calculate RMSE like the Erlang version
+                step_error = sum((o - c) ** 2 for o, c in zip(output, correct))
+                step_rmse = math.sqrt(step_error)  # This is the key change!
+
+                print(f"XOR PATTERN: Input={self.data[self.index][0]} → Network={output} → Expected={correct}")
+
+                self.index += 1
+
+                if self.index >= len(self.data):
+                    # Episode finished - calculate final fitness
+                    total_rmse = math.sqrt(self.error_acc + step_rmse)  # Not step_error!
+                    fitness = 1.0 / (total_rmse + 1e-5)
+                    await from_pid.send(("result", fitness, 1))
+                    self.index = 0
+                    self.error_acc = 0.0
+                else:
+                    # Continue episode
+                    self.error_acc += step_rmse  # Add RMSE, not raw error
+                    await from_pid.send(("result", 0.0, 0))
+
+            elif tag == "terminate":
+                return

experiments/stochastic_hillclimber/actors/sensor.py

@@ -0,0 +1,45 @@
+# actors/sensor.py
+from actor import Actor
+import random
+
+
+class Sensor(Actor):
+    def __init__(self, sid, cx_pid, name, vector_length, fanout_pids, scape=None):
+        super().__init__(f"Sensor-{sid}")
+        self.sid = sid
+        self.cx_pid = cx_pid
+        self.sname = name
+        self.vl = vector_length
+        self.fanout = fanout_pids
+        self.scape = scape
+
+    async def run(self):
+        while True:
+            print("sensor running...")
+            msg = await self.inbox.get()
+            tag = msg[0]
+
+            print("got sensor message: ", msg)
+
+            if tag == "sync":
+                vec = await self._sense()
+                print("sensed vec: ", vec)
+                for pid in self.fanout:
+                    await pid.send(("forward", self.sid, vec))
+
+            elif tag == "terminate":
+                return
+
+    async def _sense(self):
+        if self.sname == "rng":
+            return [random.random() for _ in range(self.vl)]
+        elif self.sname == "xor_GetInput" and self.scape:
+            print("TODO")
+            await self.scape.send(("sense", self.sid, self))
+            msg = await self.inbox.get()
+            if msg[0] == "percept":
+                return msg[1]
+            else:
+                return [0.0] * self.vl
+        else:
+            return [0.0] * self.vl

experiments/stochastic_hillclimber/actors/test.json

@@ -0,0 +1,179 @@
+{
+  "cortex": {
+    "id": 5.685689537782505e-10,
+    "sensor_ids": [
+      5.685689537782522e-10
+    ],
+    "actuator_ids": [
+      5.685689537782516e-10
+    ],
+    "neuron_ids": [
+      5.685689537782498e-10,
+      5.685689537782496e-10,
+      5.685689537782496e-10,
+      5.685689537782457e-10,
+      5.685689537782457e-10,
+      5.685689537782437e-10
+    ]
+  },
+  "sensor": {
+    "id": 5.685689537782522e-10,
+    "name": "xor_GetInput",
+    "vector_length": 2,
+    "cx_id": 5.685689537782505e-10,
+    "fanout_ids": [
+      5.685689537782498e-10,
+      5.685689537782496e-10,
+      5.685689537782496e-10
+    ]
+  },
+  "actuator": {
+    "id": 5.685689537782516e-10,
+    "name": "xor_SendOutput",
+    "vector_length": 1,
+    "cx_id": 5.685689537782505e-10,
+    "fanin_ids": [
+      5.685689537782437e-10
+    ]
+  },
+  "neurons": [
+    {
+      "id": 5.685689537782498e-10,
+      "layer_index": 0,
+      "cx_id": 5.685689537782505e-10,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 5.685689537782522e-10,
+          "weights": [
+            -0.16508088565795254,
+            -0.2129110085532152
+          ]
+        }
+      ],
+      "output_ids": [
+        5.685689537782493e-10,
+        5.685689537782493e-10
+      ]
+    },
+    {
+      "id": 5.685689537782496e-10,
+      "layer_index": 0,
+      "cx_id": 5.685689537782505e-10,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 5.685689537782522e-10,
+          "weights": [
+            -0.2097398020485577,
+            0.39878180348846304
+          ]
+        }
+      ],
+      "output_ids": [
+        5.685689537782493e-10,
+        5.685689537782493e-10
+      ]
+    },
+    {
+      "id": 5.685689537782496e-10,
+      "layer_index": 0,
+      "cx_id": 5.685689537782505e-10,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 5.685689537782522e-10,
+          "weights": [
+            -0.29051024087142796,
+            0.4799280128038572
+          ]
+        }
+      ],
+      "output_ids": [
+        5.685689537782493e-10,
+        5.685689537782493e-10
+      ]
+    },
+    {
+      "id": 5.685689537782457e-10,
+      "layer_index": 1,
+      "cx_id": 5.685689537782505e-10,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 5.685689537782498e-10,
+          "weights": [
+            -0.13139529896484625
+          ]
+        },
+        {
+          "input_id": 5.685689537782496e-10,
+          "weights": [
+            0.0922098630405015
+          ]
+        },
+        {
+          "input_id": 5.685689537782496e-10,
+          "weights": [
+            0.07506465172430998
+          ]
+        }
+      ],
+      "output_ids": [
+        5.685689537782454e-10
+      ]
+    },
+    {
+      "id": 5.685689537782457e-10,
+      "layer_index": 1,
+      "cx_id": 5.685689537782505e-10,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 5.685689537782498e-10,
+          "weights": [
+            0.01874150935077279
+          ]
+        },
+        {
+          "input_id": 5.685689537782496e-10,
+          "weights": [
+            0.1722685772992305
+          ]
+        },
+        {
+          "input_id": 5.685689537782496e-10,
+          "weights": [
+            -0.07333424218267892
+          ]
+        }
+      ],
+      "output_ids": [
+        5.685689537782454e-10
+      ]
+    },
+    {
+      "id": 5.685689537782437e-10,
+      "layer_index": 2,
+      "cx_id": 5.685689537782505e-10,
+      "activation_function": "tanh",
+      "input_weights": [
+        {
+          "input_id": 5.685689537782457e-10,
+          "weights": [
+            0.47060152728694127
+          ]
+        },
+        {
+          "input_id": 5.685689537782457e-10,
+          "weights": [
+            0.33179877773351285
+          ]
+        }
+      ],
+      "output_ids": [
+        5.685689537782516e-10
+      ]
+    }
+  ]
+}

experiments/stochastic_hillclimber/actors/trainer.py

@@ -0,0 +1,151 @@
+# trainer.py
+import asyncio
+import time
+from typing import Any, Dict, List, Tuple, Optional
+
+import morphology  # dein morphology.py
+from genotype import construct, save_genotype, print_genotype
+from exoself import Exoself  # deine Actor-basierte Exoself-Implementierung
+
+
+class Trainer:
+    """
+    Stochastischer Hillclimber wie im Erlang-Original.
+    - morphology_spec: entweder das morphology-Modul, ein String-Key, oder eine Callable-Morphologie
+    - hidden_layer_densities: z.B. [4, 3]
+    - max_attempts / eval_limit / fitness_target: Stoppbedingungen
+    - experimental_file / best_file: Pfade, falls du wie im Buch speichern willst
+    """
+
+    def __init__(
+            self,
+            morphology_spec=morphology,
+            hidden_layer_densities: List[int] = None,
+            *,
+            max_attempts: int = 5,
+            eval_limit: float = float("inf"),
+            fitness_target: float = float("inf"),
+            experimental_file: Optional[str] = "experimental.json",
+            best_file: Optional[str] = "best.json",
+            exoself_steps_per_eval: int = 0,
+    ):
+        self.morphology_spec = morphology_spec
+        self.hds = hidden_layer_densities or []
+        self.max_attempts = max_attempts
+        self.eval_limit = eval_limit
+        self.fitness_target = fitness_target
+        self.experimental_file = experimental_file
+        self.best_file = best_file
+        # Wenn deine Exoself/Cortex eine feste Anzahl Zyklen pro „Evaluation“ braucht,
+        # kannst du hier default 0 lassen (Cortex entscheidet über Halt-Flag),
+        # oder einen Wert setzen.
+        self.exoself_steps_per_eval = exoself_steps_per_eval
+
+        # Laufende Akkus (wie im Erlang-Code)
+        self.best_fitness = float("-inf")
+        self.best_genotype: Optional[Dict[str, Any]] = None
+
+        self.eval_acc = 0
+        self.cycle_acc = 0
+        self.time_acc = 0.0
+
+    async def _run_one_attempt(self) -> Tuple[float, int, int, float, Dict[str, Any]]:
+        """
+        Ein Trainingsversuch:
+        - Genotyp konstruieren
+        - Exoself laufen lassen
+        - Fitness/Evals/Cycles/Time zurückgeben + den verwendeten Genotyp
+        """
+        print("constructing genotype...")
+        geno = construct(self.morphology_spec, self.hds, file_name=self.experimental_file, add_bias=True)
+
+        # Exoself starten und bis zum evaluation_completed warten
+        fitness, evals, cycles, elapsed = await self._evaluate_with_exoself(geno)
+
+        return fitness, evals, cycles, elapsed, geno
+
+    async def _evaluate_with_exoself(self, genotype: Dict[str, Any]) -> Tuple[float, int, int, float]:
+        """
+        Startet Exoself (deine Actor-basierte Variante) und wartet,
+        bis der Cortex die Evaluation abgeschlossen hat.
+        Erwartete Rückgabe: fitness, evals, cycles, elapsed
+        """
+        print("creating exoself...")
+        ex = Exoself(genotype)
+        # Exoself.run() sollte idealerweise einen Tuple (fitness, evals, cycles, time)
+        # liefern. Falls deine Version aktuell „backup“-Listen liefert, ersetze das hier
+        # mit der passenden Logik oder benutze das „evaluation_completed“-Signal aus dem Cortex.
+        fitness, evals, cycles, elapsed = await ex.run_evaluation()
+        return fitness, evals, cycles, elapsed
+
+    async def go(self):
+        """
+        Entspricht dem Erlang loop/…:
+        Wiederholt Versuche, bis Stoppbedingung erfüllt.
+        """
+        attempt = 1
+        while True:
+            print(".........")
+            print("current attempt: ", attempt)
+            print(".........")
+            # Stoppbedingung vor Versuch?
+            if attempt > self.max_attempts or self.eval_acc >= self.eval_limit or self.best_fitness >= self.fitness_target:
+                # Ausgabe wie im Buch
+                if self.best_genotype and self.best_file:
+                    # bestes Genotypfile ausgeben/„drucken“
+                    save_genotype(self.best_file, self.best_genotype)
+                    print_genotype(self.best_file)
+                print(
+                    f" Morphology: {getattr(self.morphology_spec, '__name__', str(self.morphology_spec))} | "
+                    f"Best Fitness: {self.best_fitness} | EvalAcc: {self.eval_acc}"
+                )
+                # Optional: an „Benchmarker“ melden – bei dir vermutlich nicht nötig
+                return {
+                    "best_fitness": self.best_fitness,
+                    "eval_acc": self.eval_acc,
+                    "cycle_acc": self.cycle_acc,
+                    "time_acc": self.time_acc,
+                    "best_file": self.best_file,
+                }
+
+            print("RUN ONE ATTEMPT!")
+            # --- Ein Versuch ---
+            fitness, evals, cycles, elapsed, geno = await self._run_one_attempt()
+
+            print("update akkus...")
+
+            # Akkus updaten
+            self.eval_acc += evals
+            self.cycle_acc += cycles
+            self.time_acc += elapsed
+
+            # Besser als bisher?
+            if fitness > self.best_fitness:
+                # „experimental.json“ → „best.json“ (semantisch wie file:rename(...))
+                self.best_fitness = fitness
+                self.best_genotype = geno
+                if self.best_file:
+                    save_genotype(self.best_file, geno)
+                # Reset Attempt-Zähler (wie Erlang: Attempt=0 nach Verbesserung)
+                attempt = 1
+            else:
+                attempt += 1
+
+
+# --------------------------
+# Beispiel: ausführen
+# --------------------------
+if __name__ == "__main__":
+    # Beispielkonfiguration (XOR-Morphologie, kleine Topologie)
+    trainer = Trainer(
+        morphology_spec=morphology,  # oder morphology.xor_mimic
+        hidden_layer_densities=[2],  # wie im Buch-Beispiel
+        max_attempts=1000,
+        eval_limit=float("inf"),
+        fitness_target=float("inf"),
+        experimental_file="experimental.json",
+        best_file="best.json",
+        exoself_steps_per_eval=0,  # 0 => Cortex/Scape steuern Halt-Flag
+    )
+
+    asyncio.run(trainer.go())