adapting mutation rate

EvolutionStrategies/main.py

@@ -4,11 +4,11 @@ import matplotlib.pyplot as plt
 import pickle
 import sys
 
-HIDDEN_LAYER = 2
+HIDDEN_LAYER = 12
 BIAS = True
 POP_SIZE = 50
 MUTATION_FACTOR = 0.1  # 0 <= x <= 1
-LEARNING_RATE = 0.03   # 0 <= x <= 1
+LEARNING_RATE = 0.02   # 0 <= x <= 1
 GENS = 7000
 MAX_STEPS = 200  # after 1600 steps the Environment gives us a done anyway.
 
@@ -31,6 +31,7 @@ def plot_reward(rewards):
 
 if __name__ == '__main__':
     avg_rewards = []
+    mutation_factors = []
 
     try:
         population = Population(POP_SIZE, HIDDEN_LAYER, BIAS, MUTATION_FACTOR, MAX_STEPS, LOAD_BRAIN, VERSION, LEARNING_RATE, RENDER_BEST)
@@ -55,9 +56,12 @@ if __name__ == '__main__':
             if gen % 10 == 0:
                 population.walker.save()
                 avg_rewards.append(population.get_walker_stats())
+                mutation_factors.append(population.mutation_factor)
                 population.walker.save_mlp_weights(gen)
                 with open(f'./models/{HIDDEN_LAYER}_{VERSION}_{POP_SIZE}_{LEARNING_RATE}_AvgRewards', 'wb') as fp:
                     pickle.dump(avg_rewards, fp)
+                with open(f'./models/{HIDDEN_LAYER}_{VERSION}_{POP_SIZE}_{LEARNING_RATE}_MutationFactors', 'wb') as fp:
+                    pickle.dump(mutation_factors, fp)
                 if gen == 1000:
                     population.lr = 0.01
                 #if gen == 5000:

EvolutionStrategies/population.py

@@ -14,7 +14,7 @@ class Population:
         self.version = version
         self.max_steps = max_steps
         self.render_best = render_best
-        self.env = gym.make('Pendulum-v1')  # MountainCarContinuous-v0  LunarLanderContinuous-v2  Pendulum-v1  CarRacing-v0
+        self.env = gym.make('BipedalWalker-v3')  # MountainCarContinuous-v0  LunarLanderContinuous-v2  Pendulum-v1  CarRacing-v0
         self.walker = Walker(hidden_layer, bias, version, load_brain, self.env)
         self.mutated_weights = dict()
         self.mutants = []
@@ -33,6 +33,12 @@ class Population:
             self.rewards[i] = self.mutants[i].get_reward(self.max_steps)
 
     def evolve(self):
+        main_reward = self.walker.get_reward(self.max_steps)
+        good_mutations = np.where(self.rewards > main_reward)
+        if len(good_mutations)/len(self.rewards) > 0.2:
+            self.mutation_factor += self.mutation_factor/10
+        elif len(good_mutations)/len(self.rewards) < 0.2:
+            self.mutation_factor -= self.mutation_factor/10
         A = (self.rewards - np.mean(self.rewards)) / np.std(self.rewards)
         weights = self.walker.get_weights()
         for i in range(self.size):