From 5e0b34b60794f9048ffda856f68154a8fc0bf948 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tobias=20D=C3=B6ring?= <tobias.doering@stud.hs-bochum.de>
Date: Tue, 1 Mar 2022 09:37:27 +0100
Subject: [PATCH] adapting mutation rate
---
EvolutionStrategies/main.py | 8 ++++++--
EvolutionStrategies/population.py | 8 +++++++-
2 files changed, 13 insertions(+), 3 deletions(-)
diff --git a/EvolutionStrategies/main.py b/EvolutionStrategies/main.py
index aa9e09f..bbf193d 100644
--- a/EvolutionStrategies/main.py
+++ b/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:
diff --git a/EvolutionStrategies/population.py b/EvolutionStrategies/population.py
index 0c48247..33e2bb7 100644
--- a/EvolutionStrategies/population.py
+++ b/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):
--
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