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evolution_strategy_hebb.py
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evolution_strategy_hebb.py
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import numpy as np
import multiprocessing as mp
import torch
import time
from os.path import exists
from os import mkdir
from gym.spaces import Discrete, Box
import gym
import pybullet_envs
from fitness_functions import fitness_hebb
def compute_ranks(x):
"""
Returns rank as a vector of len(x) with integers from 0 to len(x)
"""
assert x.ndim == 1
ranks = np.empty(len(x), dtype=int)
ranks[x.argsort()] = np.arange(len(x))
return ranks
def compute_centered_ranks(x):
"""
Maps x to [-0.5, 0.5] and returns the rank
"""
y = compute_ranks(x.ravel()).reshape(x.shape).astype(np.float32)
y /= (x.size - 1)
y -= .5
return y
def worker_process_hebb(arg):
get_reward_func, hebb_rule, eng, init_weights, coeffs = arg
wp = np.array(coeffs)
decay = - 0.01 * np.mean(wp**2)
r = get_reward_func( hebb_rule, eng, init_weights, coeffs) + decay
return r
def worker_process_hebb_coevo(arg):
get_reward_func, hebb_rule, eng, init_weights, coeffs, coevolved_parameters = arg
wp = np.array(coeffs)
decay = - 0.01 * np.mean(wp**2)
r = get_reward_func( hebb_rule, eng, init_weights, coeffs, coevolved_parameters) + decay
return r
class EvolutionStrategyHebb(object):
def __init__(self, hebb_rule, environment, init_weights = 'uni', population_size=100, sigma=0.1, learning_rate=0.2, decay=0.995, num_threads=1, distribution = 'normal'):
self.hebb_rule = hebb_rule
self.environment = environment
self.init_weights = init_weights
self.POPULATION_SIZE = population_size
self.SIGMA = sigma
self.learning_rate = learning_rate
self.decay = decay
self.num_threads = mp.cpu_count() if num_threads == -1 else num_threads
self.update_factor = self.learning_rate / (self.POPULATION_SIZE * self.SIGMA)
self.distribution = distribution
# The number of hebbian coefficients per synapse
if hebb_rule == 'A':
self.coefficients_per_synapse = 1
elif hebb_rule == 'AD':
self.coefficients_per_synapse = 2
elif hebb_rule == 'AD_lr':
self.coefficients_per_synapse = 3
elif hebb_rule == 'ABC':
self.coefficients_per_synapse = 3
elif hebb_rule == 'ABC_lr':
self.coefficients_per_synapse = 4
elif hebb_rule == 'ABCD':
self.coefficients_per_synapse = 4
elif hebb_rule == 'ABCD_lr':
self.coefficients_per_synapse = 5
elif hebb_rule == 'ABCD_lr_D_out':
self.coefficients_per_synapse = 5
elif hebb_rule == 'ABCD_lr_D_in_and_out':
self.coefficients_per_synapse = 6
else:
raise ValueError('The provided Hebbian rule is not valid')
# Look up observation and action space dimension
env = gym.make(environment)
if len(env.observation_space.shape) == 3: # Pixel-based environment
self.pixel_env = True
elif len(env.observation_space.shape) == 1: # State-based environment
self.pixel_env = False
input_dim = env.observation_space.shape[0]
elif isinstance(env.observation_space, Discrete):
self.pixel_env = False
input_dim = env.observation_space.n
else:
raise ValueError('Observation space not supported')
if isinstance(env.action_space, Box):
action_dim = env.action_space.shape[0]
elif isinstance(env.action_space, Discrete):
action_dim = env.action_space.n
else:
raise ValueError('Action space not supported')
# Intial weights co-evolution flag:
self.coevolve_init = True if self.init_weights == 'coevolve' else False
if self.coevolve_init:
print('\nCo-evolving initial weights of the network')
# Initialize the values of hebbian coefficients and CNN parameters or initial weights of co-evolving initial weights
# Pixel-based environments (CNN + MLP)
if self.pixel_env:
cnn_weights = 1362 # CNN: (6, 3, 3, 3) + (8, 6, 5, 5) = 162+1200 = 1362
plastic_weights = (128*648) + (64*128) + (action_dim*64) # Hebbian coefficients: MLP x coefficients_per_synapse : plastic_weights x coefficients_per_synapse
# Co-evolution of initial weights
if self.coevolve_init:
if self.distribution == 'uniform':
self.coeffs = np.random.uniform(-1,1,(plastic_weights, self.coefficients_per_synapse))
self.initial_weights_co = np.random.uniform(-1,1, (cnn_weights + plastic_weights ,1))
elif self.distribution == 'normal':
self.coeffs = torch.randn(plastic_weights, self.coefficients_per_synapse).detach().numpy().squeeze()
self.initial_weights_co = torch.randn(cnn_weights + plastic_weights , 1).detach().numpy().squeeze()
# Random initial weights
else:
if self.distribution == 'uniform':
self.coeffs = np.random.uniform(-1,1,(plastic_weights, self.coefficients_per_synapse))
self.initial_weights_co = np.random.uniform(-1,1,(cnn_weights,1))
elif self.distribution == 'normal':
self.coeffs = torch.randn(plastic_weights, self.coefficients_per_synapse).detach().numpy().squeeze()
self.initial_weights_co = torch.randn(cnn_weights, 1).detach().numpy().squeeze()
# State-vector environments (MLP)
else:
plastic_weights = (128*input_dim) + (64*128) + (action_dim*64) # Hebbian coefficients: MLP x coefficients_per_synapse :plastic_weights x coefficients_per_synapse
# Co-evolution of initial weights
if self.coevolve_init:
if self.distribution == 'uniform':
self.coeffs = np.random.uniform(-1,1,(plastic_weights, self.coefficients_per_synapse))
self.initial_weights_co = np.random.uniform(-1,1, (plastic_weights ,1))
elif self.distribution == 'normal':
self.coeffs = torch.randn(plastic_weights, self.coefficients_per_synapse).detach().numpy().squeeze()
self.initial_weights_co = torch.randn(plastic_weights , 1).detach().numpy().squeeze()
# Random initial weights
else:
if self.distribution == 'uniform':
self.coeffs = np.random.uniform(-1,1,(plastic_weights, self.coefficients_per_synapse))
elif self.distribution == 'normal':
self.coeffs = torch.randn(plastic_weights, self.coefficients_per_synapse).detach().numpy().squeeze()
# Load fitness function for the selected environment
self.get_reward = fitness_hebb
def _get_params_try(self, w, p):
param_try = []
for index, i in enumerate(p):
jittered = self.SIGMA * i
param_try.append(w[index] + jittered)
param_try = np.array(param_try).astype(np.float32)
return param_try
# return w + p*self.SIGMA
def get_coeffs(self):
return self.coeffs.astype(np.float32)
def get_coevolved_parameters(self):
return self.initial_weights_co.astype(np.float32)
def _get_population(self, coevolved_param = False):
# x_ = np.random.randn(int(self.POPULATION_SIZE/2), self.coeffs.shape[0], self.coeffs[0].shape[0])
# population = np.concatenate((x_,-1*x_)).astype(np.float32)
population = []
if coevolved_param == False:
for i in range( int(self.POPULATION_SIZE/2) ):
x = []
x2 = []
for w in self.coeffs:
j = np.random.randn(*w.shape) # j: (coefficients_per_synapse, 1) eg. (5,1)
x.append(j) # x: (coefficients_per_synapse, number of synapses) eg. (92690, 5)
x2.append(-j)
population.append(x) # population : (population size, coefficients_per_synapse, number of synapses), eg. (10, 92690, 5)
population.append(x2)
elif coevolved_param == True:
for i in range( int(self.POPULATION_SIZE/2) ):
x = []
x2 = []
for w in self.initial_weights_co:
j = np.random.randn(*w.shape)
x.append(j)
x2.append(-j)
population.append(x)
population.append(x2)
return np.array(population).astype(np.float32)
def _get_rewards(self, pool, population):
if pool is not None:
worker_args = []
for p in population:
heb_coeffs_try1 = []
for index, i in enumerate(p):
jittered = self.SIGMA * i
heb_coeffs_try1.append(self.coeffs[index] + jittered)
heb_coeffs_try = np.array(heb_coeffs_try1).astype(np.float32)
worker_args.append( (self.get_reward, self.hebb_rule, self.environment, self.init_weights, heb_coeffs_try) )
rewards = pool.map(worker_process_hebb, worker_args)
else:
rewards = []
for p in population:
heb_coeffs_try = np.array(self._get_params_try(self.coeffs, p))
rewards.append(self.get_reward( self.hebb_rule, self.environment, self.init_weights, heb_coeffs_try))
rewards = np.array(rewards).astype(np.float32)
return rewards
def _get_rewards_coevolved(self, pool, population, population_coevolved):
if pool is not None:
worker_args = []
for z in range(len(population)):
heb_coeffs_try1 = []
for index, i in enumerate(population[z]):
jittered = self.SIGMA * i
heb_coeffs_try1.append(self.coeffs[index] + jittered)
heb_coeffs_try = np.array(heb_coeffs_try1).astype(np.float32)
coevolved_parameters_try1 = []
for index, i in enumerate(population_coevolved[z]):
jittered = self.SIGMA * i
coevolved_parameters_try1.append(self.initial_weights_co[index] + jittered)
coevolved_parameters_try = np.array(coevolved_parameters_try1).astype(np.float32)
worker_args.append( (self.get_reward, self.hebb_rule, self.environment, self.init_weights, heb_coeffs_try, coevolved_parameters_try) )
rewards = pool.map(worker_process_hebb_coevo, worker_args)
else:
rewards = []
for z in range(len(population)):
heb_coeffs_try = np.array(self._get_params_try(self.coeffs, population[z]))
coevolved_parameters_try = np.array(self._get_params_try(self.initial_weights_co, population_coevolved[z]))
rewards.append(self.get_reward( self.hebb_rule, self.environment, self.init_weights, heb_coeffs_try, coevolved_parameters_try))
rewards = np.array(rewards).astype(np.float32)
return rewards
def _update_coeffs(self, rewards, population):
rewards = compute_centered_ranks(rewards)
std = rewards.std()
if std == 0:
raise ValueError('Variance should not be zero')
rewards = (rewards - rewards.mean()) / std
for index, c in enumerate(self.coeffs):
layer_population = np.array([p[index] for p in population])
self.update_factor = self.learning_rate / (self.POPULATION_SIZE * self.SIGMA)
self.coeffs[index] = c + self.update_factor * np.dot(layer_population.T, rewards).T
if self.learning_rate > 0.001:
self.learning_rate *= self.decay
#Decay sigma
if self.SIGMA>0.01:
self.SIGMA *= 0.999
def _update_coevolved_param(self, rewards, population):
rewards = compute_centered_ranks(rewards)
std = rewards.std()
if std == 0:
raise ValueError('Variance should not be zero')
rewards = (rewards - rewards.mean()) / std
for index, w in enumerate(self.initial_weights_co):
layer_population = np.array([p[index] for p in population])
self.update_factor = self.learning_rate / (self.POPULATION_SIZE * self.SIGMA)
self.initial_weights_co[index] = w + self.update_factor * np.dot(layer_population.T, rewards).T
def run(self, iterations, print_step=10, path='heb_coeffs'):
id_ = str(int(time.time()))
if not exists(path + '/' + id_):
mkdir(path + '/' + id_)
print('Run: ' + id_ + '\n\n........................................................................\n')
pool = mp.Pool(self.num_threads) if self.num_threads > 1 else None
generations_rewards = []
for iteration in range(iterations): # Algorithm 2. Salimans, 2017: https://arxiv.org/abs/1703.03864
# Evolution of Hebbian coefficients & coevolution of cnn parameters and/or initial weights
if self.pixel_env or self.coevolve_init:
population = self._get_population() # Sample normal noise: Step 5
population_coevolved = self._get_population(coevolved_param=True) # Sample normal noise: Step 5
rewards = self._get_rewards_coevolved(pool, population, population_coevolved) # Compute population fitness: Step 6
self._update_coeffs(rewards, population) # Update coefficients: Steps 8->12
self._update_coevolved_param(rewards, population_coevolved) # Update coevolved parameters: Steps 8->12
# Evolution of Hebbian coefficients
else:
population = self._get_population() # Sample normal noise: Step 5
rewards = self._get_rewards(pool, population) # Compute population fitness: Step 6
self._update_coeffs(rewards, population) # Update coefficients: Steps 8->12
# Print fitness and save Hebbian coefficients and/or Coevolved / CNNs parameters
if (iteration + 1) % print_step == 0:
rew_ = rewards.mean()
print('iter %4i | reward: %3i | update_factor: %f lr: %f | sum_coeffs: %i sum_abs_coeffs: %4i' % (iteration + 1, rew_ , self.update_factor, self.learning_rate, int(np.sum(self.coeffs)), int(np.sum(abs(self.coeffs)))), flush=True)
if rew_ > 100:
torch.save(self.get_coeffs(), path + "/"+ id_ + '/HEBcoeffs__' + self.environment + "__rew_" + str(int(rew_)) + '__' + self.hebb_rule + "__init_" + str(self.init_weights) + "__pop_" + str(self.POPULATION_SIZE) + '__coeffs' + "__{}.dat".format(iteration))
if self.coevolve_init:
torch.save(self.get_coevolved_parameters(), path + "/"+ id_ + '/HEBcoeffs__' + self.environment + "__rew_" + str(int(rew_)) + '__' + self.hebb_rule + "__init_" + str(self.init_weights) + "__pop_" + str(self.POPULATION_SIZE) + '__coevolved_initial_weights' + "__{}.dat".format(iteration))
elif self.pixel_env:
torch.save(self.get_coevolved_parameters(), path + "/"+ id_ + '/HEBcoeffs__' + self.environment + "__rew_" + str(int(rew_)) + '__' + self.hebb_rule + "__init_" + str(self.init_weights) + "__pop_" + str(self.POPULATION_SIZE) + '__CNN_parameters' + "__{}.dat".format(iteration))
generations_rewards.append(rew_)
np.save(path + "/"+ id_ + '/Fitness_values_' + id_ + '_' + self.environment + '.npy', np.array(generations_rewards))
if pool is not None:
pool.close()
pool.join()