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tutorial_DQN_variants.py

"""
DQN and its variants
------------------------
We implement Double DQN, Dueling DQN and Noisy DQN here.

The max operator in standard DQN uses the same values both to select and to
evaluate an action by
Q(s_t, a_t) = R_{t+1} + \gamma * max_{a}Q_{tar}(s_{t+1}, a).
Double DQN propose to use following evaluation to address overestimation problem
of max operator:
Q(s_t, a_t) = R_{t+1} + \gamma * Q_{tar}(s_{t+1}, max_{a}Q(s_{t+1}, a)).

Dueling DQN uses dueling architecture where the value of state and the advantage
of each action is estimated separately.

Noisy DQN propose to explore by adding parameter noises.


Reference:
------------------------
1. Double DQN
    Van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double
    q-learning[C]//Thirtieth AAAI Conference on Artificial Intelligence. 2016.
2. Dueling DQN
    Wang Z, Schaul T, Hessel M, et al. Dueling network architectures for deep
    reinforcement learning[J]. arXiv preprint arXiv:1511.06581, 2015.
3. Noisy DQN
    Plappert M, Houthooft R, Dhariwal P, et al. Parameter space noise for
    exploration[J]. arXiv preprint arXiv:1706.01905, 2017.


Environment:
------------------------
Cartpole and Pong in OpenAI Gym


Requirements:
------------------------
tensorflow>=2.0.0a0
tensorlayer>=2.0.0


To run:
------------------------
python tutorial_DQN_variantes.py --mode=train
python tutorial_DQN_variantes.py --mode=test --save_path=dqn_variants/8000.npz
"""
import argparse
import os
import random
import time

import numpy as np
import tensorflow as tf

import tensorlayer as tl
from tutorial_wrappers import build_env

parser = argparse.ArgumentParser()
parser.add_argument('--mode', help='train or test', default='train')
parser.add_argument(
    '--save_path', default='dqn_variants', help='folder to save if mode == train else model path,'
    'qnet will be saved once target net update'
)
parser.add_argument('--seed', help='random seed', type=int, default=0)
parser.add_argument('--env_id', default='CartPole-v0', help='CartPole-v0 or PongNoFrameskip-v4')
parser.add_argument('--noisy_scale', type=float, default=1e-2)
parser.add_argument('--disable_double', action='store_false', default=True)
parser.add_argument('--disable_dueling', action='store_false', default=True)
args = parser.parse_args()

if args.mode == 'train':
    os.makedirs(args.save_path, exist_ok=True)
random.seed(args.seed)
np.random.seed(args.seed)
tf.random.set_seed(args.seed)  # reproducible
env_id = args.env_id
env = build_env(env_id, seed=args.seed)
noise_scale = args.noisy_scale
double = not args.disable_double
dueling = not args.disable_dueling

# ####################  hyper parameters  ####################
if env_id == 'CartPole-v0':
    qnet_type = 'MLP'
    number_timesteps = 10000  # total number of time steps to train on
    explore_timesteps = 100
    # epsilon-greedy schedule, final exploit prob is 0.99
    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
    lr = 5e-3  # learning rate
    buffer_size = 1000  # replay buffer size
    target_q_update_freq = 50  # how frequency target q net update
    ob_scale = 1.0  # scale observations
    clipnorm = None
else:
    # reward will increase obviously after 1e5 time steps
    qnet_type = 'CNN'
    number_timesteps = int(1e6)  # total number of time steps to train on
    explore_timesteps = 1e5
    # epsilon-greedy schedule, final exploit prob is 0.99
    epsilon = lambda i_iter: 1 - 0.99 * min(1, i_iter / explore_timesteps)
    lr = 1e-4  # learning rate
    buffer_size = 10000  # replay buffer size
    target_q_update_freq = 200  # how frequency target q net update
    ob_scale = 1.0 / 255  # scale observations
    clipnorm = 10

in_dim = env.observation_space.shape
out_dim = env.action_space.n
reward_gamma = 0.99  # reward discount
batch_size = 32  # batch size for sampling from replay buffer
warm_start = buffer_size / 10  # sample times befor learning
noise_update_freq = 50  # how frequency param noise net update


# ##############################  Network  ####################################
class MLP(tl.models.Model):

    def __init__(self, name):
        super(MLP, self).__init__(name=name)
        self.h1 = tl.layers.Dense(64, tf.nn.tanh, in_channels=in_dim[0])
        self.qvalue = tl.layers.Dense(out_dim, in_channels=64, name='q', W_init=tf.initializers.GlorotUniform())
        self.svalue = tl.layers.Dense(1, in_channels=64, name='s', W_init=tf.initializers.GlorotUniform())
        self.noise_scale = 0

    def forward(self, ni):
        feature = self.h1(ni)

        # apply noise to all linear layer
        if self.noise_scale != 0:
            noises = []
            for layer in [self.qvalue, self.svalue]:
                for var in layer.trainable_weights:
                    noise = tf.random.normal(tf.shape(var), 0, self.noise_scale)
                    noises.append(noise)
                    var.assign_add(noise)

        qvalue = self.qvalue(feature)
        svalue = self.svalue(feature)

        if self.noise_scale != 0:
            idx = 0
            for layer in [self.qvalue, self.svalue]:
                for var in layer.trainable_weights:
                    var.assign_sub(noises[idx])
                    idx += 1

        if dueling:
            # dueling network
            return svalue + qvalue - tf.reduce_mean(qvalue, 1, keepdims=True)
        else:
            return qvalue


class CNN(tl.models.Model):

    def __init__(self, name):
        super(CNN, self).__init__(name=name)
        h, w, in_channels = in_dim
        dense_in_channels = 64 * ((h - 28) // 8) * ((w - 28) // 8)
        self.conv1 = tl.layers.Conv2d(
            32, (8, 8), (4, 4), tf.nn.relu, 'VALID', in_channels=in_channels, name='conv2d_1',
            W_init=tf.initializers.GlorotUniform()
        )
        self.conv2 = tl.layers.Conv2d(
            64, (4, 4), (2, 2), tf.nn.relu, 'VALID', in_channels=32, name='conv2d_2',
            W_init=tf.initializers.GlorotUniform()
        )
        self.conv3 = tl.layers.Conv2d(
            64, (3, 3), (1, 1), tf.nn.relu, 'VALID', in_channels=64, name='conv2d_3',
            W_init=tf.initializers.GlorotUniform()
        )
        self.flatten = tl.layers.Flatten(name='flatten')
        self.preq = tl.layers.Dense(
            256, tf.nn.relu, in_channels=dense_in_channels, name='pre_q', W_init=tf.initializers.GlorotUniform()
        )
        self.qvalue = tl.layers.Dense(out_dim, in_channels=256, name='q', W_init=tf.initializers.GlorotUniform())
        self.pres = tl.layers.Dense(
            256, tf.nn.relu, in_channels=dense_in_channels, name='pre_s', W_init=tf.initializers.GlorotUniform()
        )
        self.svalue = tl.layers.Dense(1, in_channels=256, name='state', W_init=tf.initializers.GlorotUniform())
        self.noise_scale = 0

    def forward(self, ni):
        feature = self.flatten(self.conv3(self.conv2(self.conv1(ni))))

        # apply noise to all linear layer
        if self.noise_scale != 0:
            noises = []
            for layer in [self.preq, self.qvalue, self.pres, self.svalue]:
                for var in layer.trainable_weights:
                    noise = tf.random.normal(tf.shape(var), 0, self.noise_scale)
                    noises.append(noise)
                    var.assign_add(noise)

        qvalue = self.qvalue(self.preq(feature))
        svalue = self.svalue(self.pres(feature))

        if self.noise_scale != 0:
            idx = 0
            for layer in [self.preq, self.qvalue, self.pres, self.svalue]:
                for var in layer.trainable_weights:
                    var.assign_sub(noises[idx])
                    idx += 1

        if dueling:
            # dueling network
            return svalue + qvalue - tf.reduce_mean(qvalue, 1, keepdims=True)
        else:
            return qvalue


# ##############################  Replay  ####################################
class ReplayBuffer(object):

    def __init__(self, size):
        self._storage = []   #保存的容器
        self._maxsize = size #容器最大的size
        self._next_idx = 0   #指针，表示当前新增位置

    #查询这个容器的大小
    def __len__(self):
        return len(self._storage)

    #把信息放入buffer
    def add(self, *args):
        #如果当前指针大于容器目前大小，那么扩展容器，append数据
        if self._next_idx >= len(self._storage):
            self._storage.append(args)
        #如果不是，直接写进去就可以了。
        else:
            self._storage[self._next_idx] = args
        #这是一个循环指针
        self._next_idx = (self._next_idx + 1) % self._maxsize

    #对
    def _encode_sample(self, idxes):
        b_o, b_a, b_r, b_o_, b_d = [], [], [], [], []
        for i in idxes:
            o, a, r, o_, d = self._storage[i]
            b_o.append(o)
            b_a.append(a)
            b_r.append(r)
            b_o_.append(o_)
            b_d.append(d)
        return (
            np.stack(b_o).astype('float32') * ob_scale,
            np.stack(b_a).astype('int32'),
            np.stack(b_r).astype('float32'),
            np.stack(b_o_).astype('float32') * ob_scale,
            np.stack(b_d).astype('float32'),
        )

    #抽取数据
    def sample(self, batch_size):
        indexes = range(len(self._storage))
        idxes = [random.choice(indexes) for _ in range(batch_size)]
        return self._encode_sample(idxes)


# #############################  Functions  ###################################
def huber_loss(x):
    """Loss function for value"""
    return tf.where(tf.abs(x) < 1, tf.square(x) * 0.5, tf.abs(x) - 0.5)


def sync(net, net_tar):
    """Copy q network to target q network"""
    for var, var_tar in zip(net.trainable_weights, net_tar.trainable_weights):
        var_tar.assign(var)


def log_softmax(x, dim):
    temp = x - np.max(x, dim, keepdims=True)
    return temp - np.log(np.exp(temp).sum(dim, keepdims=True))


def softmax(x, dim):
    temp = np.exp(x - np.max(x, dim, keepdims=True))
    return temp / temp.sum(dim, keepdims=True)


# ###############################  DQN  #####################################
class DQN(object):

    def __init__(self):
        model = MLP if qnet_type == 'MLP' else CNN
        self.qnet = model('q')
        if args.mode == 'train':
            self.qnet.train()
            self.targetqnet = model('targetq')
            self.targetqnet.infer()
            sync(self.qnet, self.targetqnet)
        else:
            self.qnet.infer()
            tl.files.load_and_assign_npz(name=args.save_path, network=self.qnet)
        self.niter = 0
        if clipnorm is not None:
            self.optimizer = tf.optimizers.Adam(learning_rate=lr, clipnorm=clipnorm)
        else:
            self.optimizer = tf.optimizers.Adam(learning_rate=lr)
        self.noise_scale = noise_scale

    def get_action(self, obv):
        eps = epsilon(self.niter)
        if args.mode == 'train':
            if random.random() < eps:
                return int(random.random() * out_dim)
            obv = np.expand_dims(obv, 0).astype('float32') * ob_scale
            if self.niter < explore_timesteps:
                self.qnet.noise_scale = self.noise_scale
                q_ptb = self._qvalues_func(obv).numpy()
                self.qnet.noise_scale = 0
                if i % noise_update_freq == 0:
                    q = self._qvalues_func(obv).numpy()
                    kl_ptb = (log_softmax(q, 1) - log_softmax(q_ptb, 1))
                    kl_ptb = np.sum(kl_ptb * softmax(q, 1), 1).mean()
                    kl_explore = -np.log(1 - eps + eps / out_dim)
                    if kl_ptb < kl_explore:
                        self.noise_scale *= 1.01
                    else:
                        self.noise_scale /= 1.01
                return q_ptb.argmax(1)[0]
            else:
                return self._qvalues_func(obv).numpy().argmax(1)[0]
        else:
            obv = np.expand_dims(obv, 0).astype('float32') * ob_scale
            return self._qvalues_func(obv).numpy().argmax(1)[0]

    @tf.function
    def _qvalues_func(self, obv):
        return self.qnet(obv)

    def train(self, b_o, b_a, b_r, b_o_, b_d):
        self._train_func(b_o, b_a, b_r, b_o_, b_d)

        self.niter += 1
        if self.niter % target_q_update_freq == 0:
            sync(self.qnet, self.targetqnet)
            path = os.path.join(args.save_path, '{}.npz'.format(self.niter))
            tl.files.save_npz(self.qnet.trainable_weights, name=path)

    @tf.function
    def _train_func(self, b_o, b_a, b_r, b_o_, b_d):
        with tf.GradientTape() as tape:
            td_errors = self._tderror_func(b_o, b_a, b_r, b_o_, b_d)
            loss = tf.reduce_mean(huber_loss(td_errors))

        grad = tape.gradient(loss, self.qnet.trainable_weights)
        self.optimizer.apply_gradients(zip(grad, self.qnet.trainable_weights))

        return td_errors

    @tf.function
    def _tderror_func(self, b_o, b_a, b_r, b_o_, b_d):
        if double:
            b_a_ = tf.one_hot(tf.argmax(self.qnet(b_o_), 1), out_dim)
            b_q_ = (1 - b_d) * tf.reduce_sum(self.targetqnet(b_o_) * b_a_, 1)
        else:
            b_q_ = (1 - b_d) * tf.reduce_max(self.targetqnet(b_o_), 1)

        b_q = tf.reduce_sum(self.qnet(b_o) * tf.one_hot(b_a, out_dim), 1)
        return b_q - (b_r + reward_gamma * b_q_)


# #############################  Trainer  ###################################
if __name__ == '__main__':
    dqn = DQN()
    if args.mode == 'train':
        buffer = ReplayBuffer(buffer_size)

        o = env.reset()
        nepisode = 0
        t = time.time()
        for i in range(1, number_timesteps + 1):

            a = dqn.get_action(o)

            # execute action and feed to replay buffer
            # note that `_` tail in var name means next
            o_, r, done, info = env.step(a)
            buffer.add(o, a, r, o_, done)

            if i >= warm_start:
                transitions = buffer.sample(batch_size)
                dqn.train(*transitions)

            if done:
                o = env.reset()
            else:
                o = o_

            # episode in info is real (unwrapped) message
            if info.get('episode'):
                nepisode += 1
                reward, length = info['episode']['r'], info['episode']['l']
                fps = int(length / (time.time() - t))
                print(
                    'Time steps so far: {}, episode so far: {}, '
                    'episode reward: {:.4f}, episode length: {}, FPS: {}'.format(i, nepisode, reward, length, fps)
                )
                t = time.time()
    else:
        nepisode = 0
        o = env.reset()
        for i in range(1, number_timesteps + 1):
            a = dqn.get_action(o)

            # execute action
            # note that `_` tail in var name means next
            o_, r, done, info = env.step(a)

            if done:
                o = env.reset()
            else:
                o = o_

            # episode in info is real (unwrapped) message
            if info.get('episode'):
                nepisode += 1
                reward, length = info['episode']['r'], info['episode']['l']
                print(
                    'Time steps so far: {}, episode so far: {}, '
                    'episode reward: {:.4f}, episode length: {}'.format(i, nepisode, reward, length)
                )