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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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