Formats train genetic

train_GeneticAgent.py

@@ -3,26 +3,35 @@
 import multiprocessing
 from environment.tetris import Tetris
 from agents.GeneticAgent import GeneticAgent
+
 multiprocessing.freeze_support()
 from utils.plot import ScatterPlot
 
-level_multi     = False
-cols            = 10
-rows            = 20
-state_size      = 5
+level_multi = False
+cols = 10
+rows = 20
+state_size = 5
 
 population_size = 20
-elite_pct       = 0.1
-parent_pct      = 0.8
+elite_pct = 0.1
+parent_pct = 0.8
 
-num_gens        = 10
-max_steps       = 1000
+num_gens = 10
+max_steps = 1000
 
-mutation_value  = 0.02
+mutation_value = 0.02
 mutation_chance = 0.9
-crossover_rate  = 0.7
+crossover_rate = 0.7
 
-agent = GeneticAgent(state_size, elite_pct, population_size, max_steps, num_gens, mutation_value, mutation_chance)
+agent = GeneticAgent(
+    state_size,
+    elite_pct,
+    population_size,
+    max_steps,
+    num_gens,
+    mutation_value,
+    mutation_chance,
+)
 
 
 def train():
@@ -37,30 +46,31 @@ def train():
             scores = np.array(list(tqdm(pool.imap_unordered(agent.get_fitness, args), desc='Population done', leave=False)))
 
         agent.weights = agent.weights[scores.argsort()[::-1]]
-        
+
         all_scores[gen] = scores
-        survivors = int(np.ceil(elite_pct*population_size))
-        parents = agent.weights[:int(population_size*parent_pct)-survivors]
-        
+        survivors = int(np.ceil(elite_pct * population_size))
+        parents = agent.weights[: int(population_size * parent_pct) - survivors]
+
         for i in range(survivors, population_size, 2):
             idx = np.random.randint(0, len(parents), size=2)
-            parent1, parent2 = parents[idx,:]
-            
-        best_per_gen[gen] = agent.weights[scores.argmax()]
+            parent1, parent2 = parents[idx, :]
+
+            best_per_gen[gen] = agent.weights[scores.argmax()]
             if np.random.random() < crossover_rate:
                 child1, child2 = agent.breed(parent1, parent2)
-                
-            else:   
+
+            else:
                 child1, child2 = parent1, parent2
-            
+
             agent.weights[i] = child1
             if i + 1 < population_size:
-                agent.weights[i+1] = child2
+                agent.weights[i + 1] = child2
         for i, score in enumerate(scores):
-            plot.add_point(i+gen*population_size,score, True)
+            plot.add_point(i + gen * population_size, score, True)
     tqdm.write(f"Final best weights: {agent.weights[0]}")
     plot.save("genetic_scores.csv")
     np.savetxt("best_weights_per_gen.csv", best_per_gen, delimiter=",")
 
-if __name__ == '__main__': 
+
+if __name__ == "__main__":
     train()