last workig state.

experiments/car_racing_test/run.py

@@ -6,6 +6,7 @@ obs, info = env.reset()
 for _ in range(1000):
     action = env.action_space.sample()
     _, reward, terminated, truncated, _ = env.step(action)
+
     if terminated or truncated:
         obs, info = env.reset()
 

experiments/phenotype_genotype_map/pehno_geno_map.py

@@ -60,7 +60,7 @@ class Sensor(Actor):
                 if self.sname == "rng":
                     vec = rng_vector(self.vl)
                 else:
-                    # place for own sensors (this will be replaced by scapes down the road)
+                    # place for own sensors (this will be replaced by envs down the road)
                     vec = [0.0] * self.vl
                 # forward an alle Fanouts
                 for pid in self.fanout:

experiments/stochastic_hillclimber/genotype.py

@@ -1,4 +1,4 @@
-# genotype.py
+# genotype.py.old
 import json
 import math
 import random

experiments/stochastic_hillclimber/mtest.py

@@ -1,37 +1,20 @@
 import asyncio
 
 import morphology
-from experiments.stochastic_hillclimber.actors.trainer import Trainer
+from experiments.stochastic_hillclimber.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
+        morphology_spec=morphology,  
+        hidden_layer_densities=[2],  
+        max_attempts=float("inf"),  
+        eval_limit=float("inf"),  
+        fitness_target=99.9,  
         experimental_file="experimental.json",
         best_file="best.json",
-        exoself_steps_per_eval=0,  # 0 = Scape/Cortex entscheiden über Halt
+        exoself_steps_per_eval=0,  
     )
     asyncio.run(trainer.go())
 

experiments/stochastic_hillclimber/trainer.py

@@ -42,7 +42,7 @@ class Trainer:
         geno = construct(
             self.morphology_spec,
             self.hds,
-            file_name=self.experimental_file,  # <-- schreibt Startnetz nach experimental.json
+            file_name=self.experimental_file,
             add_bias=True
         )
         fitness, evals, cycles, elapsed = await self._evaluate_with_exoself(geno)
@@ -62,7 +62,7 @@ class Trainer:
             print(".........")
 
             if attempt > self.max_attempts or self.eval_acc >= self.eval_limit or self.best_fitness >= self.fitness_target:
-                # Abschlussausgabe wie im Buch
+
                 if self.best_file and os.path.exists(self.best_file):
                     print_genotype(self.best_file)
                 print(
@@ -86,7 +86,6 @@ class Trainer:
             self.cycle_acc += cycles
             self.time_acc += elapsed
 
-            # Besser als bisher?
             if fitness > self.best_fitness:
                 self.best_fitness = fitness
                 if self.best_file and self.experimental_file and os.path.exists(self.experimental_file):
@@ -96,7 +95,6 @@ class Trainer:
                 attempt += 1
 
 
-
 if __name__ == "__main__":
     trainer = Trainer(
         morphology_spec=morphology,