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June 12, 2017 23:11
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Frozen Lake NN Implementation
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import gym | |
import logging | |
import sys | |
import numpy as np | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.optim as optim | |
import cPickle as pickle | |
import os | |
from gym import wrappers | |
from torch.autograd import Variable | |
SEED = 1234 | |
NUM_EPISODES = 20000 | |
# Hyperparams | |
GAMMA = 0.99 | |
REWARD_CONST = 3.0 | |
LEARNING_RATE = 1e-2 | |
ENV_NAME = 'FrozenLake-v0' | |
ENV_INTERNAL_NAME = 'frozen-lake-nn' | |
CHECKPOINT_FILE_PATH = '{}-ckpt'.format(ENV_INTERNAL_NAME) | |
class Net(nn.Module): | |
def __init__(self): | |
super(Net, self).__init__() | |
self.fc1 = nn.Linear(16, 4) | |
def forward(self, x): | |
x = self.fc1(x) | |
return x | |
def save(net, optimizer, epoch): | |
state = { | |
'state_dict': net.state_dict(), | |
'optimizer': optimizer.state_dict(), | |
'epoch': epoch, | |
} | |
print("Saving checkpoint to file '{}'".format(CHECKPOINT_FILE_PATH)) | |
torch.save(state, CHECKPOINT_FILE_PATH) | |
# Returns net, optimizer, epoch | |
def load(): | |
net = Net() | |
# optimizer = optim.RMSprop(net.parameters(), lr=LEARNING_RATE, momentum=0.5) | |
optimizer = optim.Adam(net.parameters(), lr=LEARNING_RATE) | |
epoch = 0 | |
if os.path.isfile(CHECKPOINT_FILE_PATH): | |
print("Loading checkpoint from file '{}'".format(CHECKPOINT_FILE_PATH)) | |
checkpoint = torch.load(CHECKPOINT_FILE_PATH) | |
epoch = checkpoint['epoch'] | |
net.load_state_dict(checkpoint['state_dict']) | |
optimizer.load_state_dict(checkpoint['optimizer']) | |
return net, optimizer, epoch | |
# Boiler plate to get a gym object | |
def get_gym(record=False): | |
gym.undo_logger_setup() | |
logger = logging.getLogger() | |
formatter = logging.Formatter('[%(asctime)s] %(message)s') | |
handler = logging.StreamHandler(sys.stderr) | |
handler.setFormatter(formatter) | |
logger.addHandler(handler) | |
logger.setLevel(logging.INFO) | |
outdir = '{}-data'.format(ENV_INTERNAL_NAME) | |
env = gym.make(ENV_NAME) | |
if record: | |
env = wrappers.Monitor(env, directory=outdir, force=True) | |
return env | |
# Return a one-hot vector with the idx bit turned on | |
def get_oh_vector(idx): | |
state_arr = np.zeros((1, 16)) | |
state_arr[0][idx] = 1 | |
state_var = Variable(torch.Tensor(state_arr)) | |
return state_var | |
env = get_gym(record=True) | |
if SEED >= 0: | |
env.seed(SEED) | |
np.random.seed(SEED) | |
torch.manual_seed(SEED) | |
episode = 0 | |
non_zero_rewards = 0 | |
rewards = [] | |
print_stats = True | |
verbose = False | |
net, optimizer, _ = load() | |
pause_training = False | |
while episode < NUM_EPISODES and not pause_training: | |
new_state = env.reset() | |
done = False | |
path_len = 0 | |
while not done: | |
optimizer.zero_grad() | |
prev_state = new_state | |
state_var = get_oh_vector(prev_state) | |
output_var = net(state_var) | |
output_arr = output_var.data.numpy() | |
if np.random.rand() < 1.0 / ((episode / 200.0) + 5.0): | |
action = env.action_space.sample() | |
else: | |
action = np.argmax(output_arr) | |
if verbose: | |
print '\n== Episode {}, State: {} =='.format(episode, prev_state) | |
print 'state vector: ', state_var.data.numpy() | |
print 'output_arr: ', output_arr | |
print 'action picked: {}'.format(action) | |
path_len = path_len + 1 | |
new_state, reward, done, _ = env.step(action) | |
new_state_var = get_oh_vector(new_state) | |
new_output_var = net(new_state_var) | |
new_output_arr = new_output_var.data.numpy() | |
expected_q_val = np.max(new_output_arr) * GAMMA + reward * REWARD_CONST | |
target_arr = np.copy(output_arr) | |
target_arr[0][action] = expected_q_val | |
target_var = Variable(torch.Tensor(target_arr)) | |
if verbose: | |
print 'New State: {}, Reward: {}'.format(new_state, reward) | |
print 'New State vec: ', new_state_var.data.numpy() | |
print 'New Output Var: ', new_output_arr | |
print 'argmax: ', np.max(new_output_arr) | |
print 'expected_q_val: ', expected_q_val | |
print 'target_arr: ', target_arr | |
loss = ((target_var - output_var)**2).sum() | |
loss.backward() | |
optimizer.step() | |
if verbose: | |
print("Episode: {}, Loss: {}".format(episode, | |
loss.data.numpy()[0])) | |
check_var = net(state_var) | |
print 'After backprop output: ', check_var.data.numpy() | |
if reward != 0: | |
non_zero_rewards = non_zero_rewards + 1 | |
# pause_training = True | |
# break | |
if done: | |
rewards.append(reward) | |
last_hundred_epochs = rewards[-100:] | |
success = sum(last_hundred_epochs) * 1.0 / len(last_hundred_epochs) | |
overall_success = sum(rewards) * 1.0 / len(rewards) | |
if print_stats and episode % 10 == 0: | |
print( | |
"Episode: {}, Success Ratio in Last 100 Epochs: {}, Overall Ratio: {}". | |
format(episode, success, overall_success)) | |
episode = episode + 1 | |
if verbose: | |
print '\n\n' | |
print('Non Zero Rewards: {}, Ratio: {}'.format( | |
non_zero_rewards, non_zero_rewards * 1.0 / NUM_EPISODES)) | |
env.close() |
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