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Added Genetic Algorithm and Evolutionary Strategies

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Brandon Rozek 2019-02-27 09:54:47 -05:00
commit 1871b3263c
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import random
import numpy as np
import rltorch
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical
import gym
from copy import deepcopy
class Policy(nn.Module):
def __init__(self, state_size, action_size):
super(Policy, self).__init__()
self.state_size = state_size
self.action_size = action_size
self.fc1 = nn.Linear(state_size, 125)
self.fc_norm = nn.LayerNorm(125)
self.fc2 = nn.Linear(125, 125)
self.fc2_norm = nn.LayerNorm(125)
self.action_prob = nn.Linear(125, action_size)
def forward(self, x):
x = F.relu(self.fc_norm(self.fc1(x)))
x = F.relu(self.fc2_norm(self.fc2(x)))
x = F.softmax(self.action_prob(x), dim = 1)
return x
env = gym.make("Acrobot-v1")
def fitness(model):
state = torch.from_numpy(env.reset()).float().unsqueeze(0)
total_reward = 0
done = False
while not done:
action_probabilities = model(state)
distribution = Categorical(action_probabilities)
action = distribution.sample().item()
next_state, reward, done, _ = env.step(action)
total_reward += reward
state = torch.from_numpy(next_state).float().unsqueeze(0)
return -total_reward
# make_model should be a function that returns a nn.Module
class Population:
def __init__(self, model, population_size, fitness_fn, learning_rate = 1e-1, sigma = 0.05):
self.model = model
self.optimizer = torch.optim.Adam(self.model.parameters(), lr = learning_rate)
self.population_size = population_size
self.sigma = sigma
self.learning_rate = learning_rate
assert self.sigma >= 0
assert self.population_size > 0
self.calculate_fitness = fitness_fn
def __iter__(self):
return self
# This function is suppose to take us to the next generation
def __next__(self):
## Generate Noise
model_dict = self.model.state_dict()
white_noise_dict = {}
noise_dict = {}
for key in model_dict.keys():
white_noise_dict[key] = torch.randn(self.population_size, *model_dict[key].shape)
noise_dict[key] = self.sigma * white_noise_dict[key]
## Generate candidate solutions
candidate_solutions = []
for i in range(self.population_size):
candidate_statedict = {}
for key in model_dict.keys():
candidate_statedict[key] = model_dict[key] + noise_dict[key][i]
candidate = Policy(self.model.state_size, self.model.action_size)
candidate.load_state_dict(candidate_statedict)
candidate_solutions.append(candidate)
## Calculate fitness
fitness_values = torch.tensor([self.calculate_fitness(x) for x in candidate_solutions])
print("Average fitness: ", fitness_values.mean())
# Mean shift, scale
fitness_values = (fitness_values - fitness_values.mean()) / (fitness_values.std() + np.finfo('float').eps)
## Insert adjustments into gradients slot
self.optimizer.zero_grad()
for name, param in self.model.named_parameters():
if param.requires_grad:
noise_dim_n = len(white_noise_dict[name].shape)
dim = np.repeat(1, noise_dim_n - 1).tolist() if noise_dim_n > 0 else []
param.grad = (white_noise_dict[name] * fitness_values.float().reshape(self.population_size, *dim)).mean(0) / self.sigma
self.optimizer.step()
return deepcopy(self.model)
p = Population(Policy(6, 3), 1000, fitness)
def iterate():
for i in range(10):
next(p)

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import random
import numpy as np
# Let's solve the function f(x, y) = -2x^2 - 3(y - 4)^2
def fitness(x):
return -2 * (x[:, 0] ** 2) - 3 * (x[:, 1] - 4)**2
class Population:
def __init__(self, initial_guess, population_size, fitness_fn, learning_rate = 1e-4, sigma = 0.1):
self.current_solution = initial_guess
self.population_size = population_size
self.sigma = sigma
self.learning_rate = learning_rate
assert self.population_size > 0
assert self.sigma >= 0
self.calculate_fitness = fitness_fn
def __iter__(self):
return self
# This function is suppose to take us to the next generation
def __next__(self):
white_noise = np.random.randn(self.population_size, *self.current_solution.shape)
noise = self.sigma * white_noise
candidate_solutions = self.current_solution + noise
fitness_values = self.calculate_fitness(candidate_solutions)
# Mean shift and scale
fitness_values = (fitness_values - np.mean(fitness_values)) / (np.std(fitness_values) + np.finfo('float').eps)
new_solution = self.current_solution + self.learning_rate * np.mean(white_noise.T * fitness_values, axis = 1) / self.sigma
self.current_solution = new_solution
return new_solution
def item(self):
return self.current_solution
def test():
guess = np.random.randn(2)
p = Population(guess, 100, fitness)
for i in range(10000):
next(p)
return p.item()

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import random
import numpy as np
import rltorch
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical
import gym
class Policy(nn.Module):
def __init__(self, state_size, action_size):
super(Policy, self).__init__()
self.state_size = state_size
self.action_size = action_size
self.fc1 = nn.Linear(state_size, 125)
self.fc_norm = nn.LayerNorm(125)
self.fc2 = nn.Linear(125, 125)
self.fc2_norm = nn.LayerNorm(125)
self.action_prob = nn.Linear(125, action_size)
def forward(self, x):
x = F.relu(self.fc_norm(self.fc1(x)))
x = F.relu(self.fc2_norm(self.fc2(x)))
x = F.softmax(self.action_prob(x), dim = 1)
return x
env = gym.make("Acrobot-v1")
def fitness(model_dict):
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
model = Policy(state_size, action_size)
model.load_state_dict(model_dict)
state = torch.from_numpy(env.reset()).float().unsqueeze(0)
total_reward = 0
done = False
while not done:
action_probabilities = model(state)
distribution = Categorical(action_probabilities)
action = distribution.sample().item()
next_state, reward, done, _ = env.step(action)
total_reward += reward
state = torch.from_numpy(next_state).float().unsqueeze(0)
return total_reward
# make_model should be a function that returns a nn.Module
class Population:
def __init__(self, model, population_size, fitness_fn, keep_best = 1, mutation_rate = 0.01, sigma = 0.1):
self.model = model
self.population_size = population_size
self.mutation_rate = mutation_rate
self.keep_best = keep_best
self.sigma = sigma
assert self.sigma >= 0
assert self.keep_best >= 0
assert self.population_size > 0
assert self.keep_best < self.population_size
self.pop = self._generate_population(model, population_size)
# Probability that an individual will last to the next generation
self.survivability = np.full(shape=(population_size), fill_value = 1 / population_size)
self.calculate_fitness = fitness_fn
def _generate_population(self, model, population_size):
pop = []
for i in range(population_size):
member = {}
for key, value in model.state_dict().items():
member[key] = value + self.sigma * torch.randn(*value.shape)
pop.append(member)
return pop
def _calculate_survivability(self, pop):
fitness = np.array(list(map(self.calculate_fitness, pop)))
# Make fitness non-negative
if fitness.min() <= 0:
fitness += (-1 * fitness.min()) + 1e-10 # Add some random constant to avoid 0 probability
return fitness / fitness.sum()
def _select_survivors(self, population, survivability):
population_size = len(population)
survivors_indices = np.random.choice(range(0, population_size), size=(population_size - self.keep_best) * 2, p=survivability)
return [population[i] for i in survivors_indices]
def _crossover(self, parents):
parent_ind = np.array(range(0, len(parents)))
parent1_ind = np.random.choice(parent_ind, size = len(parents) // 2, replace=False)
parent2_ind = np.setdiff1d(parent_ind, parent1_ind)
parent1 = [parents[i] for i in parent1_ind]
parent2 = [parents[i] for i in parent1_ind]
children = []
for parent1, parent2 in zip(parent1, parent2):
child = {}
for key in parent1.keys():
crossover_ind = random.randint(0, len(parent1[key]))
child_value = torch.cat((parent1[key][:crossover_ind], parent2[key][crossover_ind:]))
child_value = self._mutate(child_value)
child[key] = child_value
children.append(child)
return children
def _mutate(self, child):
if np.random.rand() < self.mutation_rate:
child += self.sigma * torch.randn(*child.shape)
return child
def __iter__(self):
return self
# This function is suppose to take us to the next generation
def __next__(self):
survivability = self._calculate_survivability(self.pop)
if self.keep_best > 0:
survivor_ind = np.argsort(survivability)[-self.keep_best:]
parents = self._select_survivors(self.pop, survivability)
children = self._crossover(parents)
next_pop = [self.pop[i] for i in survivor_ind] + children
self.pop = next_pop
return next_pop
def solution(self):
return self.pop[self.survivability[-1]]
def test():
p = Population(Policy(6, 3), 100, fitness)
for i in range(100):
next(p)
return p.solution()

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import random
import numpy as np
# Let's solve the function f(x, y) = -2x^2 - 3(y - 4)^2
def fitness(x):
return -2 * (x[:, 0] ** 2) - 3 * (x[:, 1] - 4)**2
class Population:
def __init__(self, output_size, population_size, fitness_fn, low = 0., high = 1., keep_best = 1, mutation_rate = 0.001):
self.population_size = population_size
self.output_size = output_size
self.low = low
self.high = high
self.mutation_rate = mutation_rate
self.keep_best = keep_best
assert self.keep_best >= 0
assert self.population_size > 0
assert self.keep_best < self.population_size
self.pop = self._generate_population(output_size, population_size, low = low, high = high)
# Probability that an individual will last to the next generation
self.survivability = np.full(shape=(population_size), fill_value = 1 / population_size)
self.calculate_fitness = fitness_fn
def _generate_population(self, output_size, population_size, low = 0., high = 1.):
return np.random.uniform(low, high, size=(population_size, output_size))
def _calculate_survivability(self, pop):
fitness = self.calculate_fitness(pop)
# Make fitness non-negative
if fitness.min() <= 0:
fitness += (-1 * fitness.min()) + np.finfo('float').eps
return fitness / fitness.sum()
def _select_survivors(self, population, survivability):
population_size = len(population)
survivors_indices = np.random.choice(range(0, population_size), size=(population_size - self.keep_best) * 2, p=survivability)
return population.take(survivors_indices, axis = 0)
def _crossover(self, parents):
parent_ind = np.array(range(0, len(parents)))
parent1_ind = np.random.choice(parent_ind, size = len(parents) // 2, replace=False)
parent2_ind = np.setdiff1d(parent_ind, parent1_ind)
parents1 = parents[parent1_ind]
parents2 = parents[parent2_ind]
children = []
for parent1, parent2 in zip(parents1, parents2):
crossover_ind = random.randint(0, self.output_size)
child = np.zeros_like(parent1)
child[:crossover_ind] = parent1[:crossover_ind]
child[crossover_ind:] = parent2[crossover_ind:]
child = self._mutate(child)
children.append(child)
return np.vstack(children)
def _mutate(self, child):
for i in range(len(child)):
if np.random.rand() < self.mutation_rate:
child[i] = np.random.uniform(self.low, self.high)
return child
def __iter__(self):
return self
# This function is suppose to take us to the next generation
def __next__(self):
survivability = self._calculate_survivability(self.pop)
if self.keep_best > 0:
survivor_ind = np.argsort(survivability)[-self.keep_best:]
parents = self._select_survivors(self.pop, survivability)
children = self._crossover(parents)
next_pop = np.concatenate((self.pop.take(survivor_ind, axis = 0), children))
self.pop = next_pop
return next_pop
def solution(self):
return self.pop.take(sorted(self.survivability)[-1], axis = 0)
def test():
p = Population(2, 100, fitness, low = -10, high = 10)
for i in range(10000):
next(p)
return p.solution()