EvolutionaryAlgo/es_model_test.py

103 lines
No EOL
3.6 KiB
Python

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)