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Tensorlayer深度强化学习系列：
Tensorlayer深度强化学习之Tensorlayer安装
【Tensorlayer系列】深度强化学习之FrozenLake介绍及表格型Q学习求解
3.1 FrozenLake-v0
FrozenLake环境的介绍可参照【Tensorlayer系列】深度强化学习之FrozenLake介绍及表格型Q学习求解，这里不再赘述。
3.2 DQN
输入：FrozenLake中一共有16个状态，分别为0~15，DQN的输入采用独热编码表示，长度为16，当状态为n时，则独热编码中对应索引位置为1，其他为0。
输出：上下左右四个动作。
3.2.1 代码
"""
Deep Q-Network Q(a, s)
-----------------------
TD Learning, Off-Policy, e-Greedy Exploration (GLIE).
Q(S, A) <- Q(S, A) + alpha * (R + lambda * Q(newS, newA) - Q(S, A))
delta_w = R + lambda * Q(newS, newA)
See David Silver RL Tutorial Lecture 5 - Q-Learning for more details.
Reference
----------
original paper: https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf
EN: https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0#.5m3361vlw
CN: https://zhuanlan.zhihu.com/p/25710327
Note: Policy Network has been proved to be better than Q-Learning, see tutorial_atari_pong.py
Environment
-----------
# The FrozenLake v0 environment
https://gym.openai.com/envs/FrozenLake-v0
The agent controls the movement of a character in a grid world. Some tiles of
the grid are walkable, and others lead to the agent falling into the water.
Additionally, the movement direction of the agent is uncertain and only partially
depends on the chosen direction. The agent is rewarded for finding a walkable
path to a goal tile.
SFFF (S: starting point, safe)
FHFH (F: frozen surface, safe)
FFFH (H: hole, fall to your doom)
HFFG (G: goal, where the frisbee is located)
The episode ends when you reach the goal or fall in a hole. You receive a reward
of 1 if you reach the goal, and zero otherwise.
Prerequisites
--------------
tensorflow>=2.0.0a0
tensorlayer>=2.0.0
To run
-------
python tutorial_DQN.py --train/test
"""
import argparse
import os
import time
import gym
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorlayer as tl
# add arguments in command --train/test
parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
parser.add_argument('--train', dest='train', action='store_true', default=True)
parser.add_argument('--test', dest='test', action='store_true', default=True)
args = parser.parse_args()
tl.logging.set_verbosity(tl.logging.DEBUG)
##################### hyper parameters ####################
env_id = 'FrozenLake-v0'
alg_name = 'DQN'
lambd = .99 # decay factor
e = 0.1 # e-Greedy Exploration, the larger the more random
num_episodes = 10000
render = False # display the game environment
rList = [] #记录奖励
##################### DQN ##########################
def to_one_hot(i, n_classes=None):
a = np.zeros(n_classes, 'uint8')
a[i] = 1
return a
## Define Q-network q(a,s) that ouput the rewards of 4 actions by given state, i.e. Action-Value Function.
# encoding for state: 4x4 grid can be represented by one-hot vector with 16 integers.
def get_model(inputs_shape):
ni = tl.layers.Input(inputs_shape, name='observation')
nn = tl.layers.Dense(4, act=None, W_init=tf.random_uniform_initializer(0, 0.01), b_init=None, name='q_a_s')(ni)
return tl.models.Model(inputs=ni, outputs=nn, name="Q-Network")
def save_ckpt(model): # save trained weights
path = os.path.join('model', '_'.join([alg_name, env_id]))
if not os.path.exists(path):
os.makedirs(path)
tl.files.save_weights_to_hdf5(os.path.join(path, 'dqn_model.hdf5'), model)
def load_ckpt(model): # load trained weights
path = os.path.join('model', '_'.join([alg_name, env_id]))
tl.files.save_weights_to_hdf5(os.path.join(path, 'dqn_model.hdf5'), model)
if __name__ == '__main__':
qnetwork = get_model([None, 16])
qnetwork.train()
train_weights = qnetwork.trainable_weights
optimizer = tf.optimizers.SGD(learning_rate=0.1)
env = gym.make(env_id)
t0 = time.time()
if args.train:
all_episode_reward = []
for i in range(num_episodes):
## Reset environment and get first new observation
s = env.reset() # observation is state, integer 0 ~ 15
rAll = 0
if render: env.render()
for j in range(99): # step index, maximum step is 99
## Choose an action by greedily (with e chance of random action) from the Q-network
allQ = qnetwork(np.asarray([to_one_hot(s, 16)], dtype=np.float32)).numpy()
a = np.argmax(allQ, 1)
## e-Greedy Exploration !!! sample random action
if np.random.rand(1) < e:
a[0] = env.action_space.sample()
## Get new state and reward from environment
s1, r, d, _ = env.step(a[0])
if render: env.render()
## Obtain the Q' values by feeding the new state through our network
Q1 = qnetwork(np.asarray([to_one_hot(s1, 16)], dtype=np.float32)).numpy()
## Obtain maxQ' and set our target value for chosen action.
maxQ1 = np.max(Q1) # in Q-Learning, policy is greedy, so we use "max" to select the next action.
targetQ = allQ
targetQ[0, a[0]] = r + lambd * maxQ1
## Train network using target and predicted Q values
# it is not real target Q value, it is just an estimation,
# but check the Q-Learning update formula:
# Q'(s,a) <- Q(s,a) + alpha(r + lambd * maxQ(s',a') - Q(s, a))
# minimizing |r + lambd * maxQ(s',a') - Q(s, a)|^2 equals to force Q'(s,a) ≈ Q(s,a)
with tf.GradientTape() as tape:
_qvalues = qnetwork(np.asarray([to_one_hot(s, 16)], dtype=np.float32))
_loss = tl.cost.mean_squared_error(targetQ, _qvalues, is_mean=False)
grad = tape.gradient(_loss, train_weights)
optimizer.apply_gradients(zip(grad, train_weights))
rAll += r
s = s1
## Reduce chance of random action if an episode is done.
if d ==True:
e = 1. / ((i / 50) + 10) # reduce e, GLIE: Greey in the limit with infinite Exploration
break
## Note that, the rewards here with random action
print('Training | Episode: {}/{} | Episode Reward: {:.4f} | Running Time: {:.4f}' \
.format(i, num_episodes, rAll, time.time() - t0))
if i == 0:
all_episode_reward.append(rAll)
else:
all_episode_reward.append(all_episode_reward[-1] * 0.9 + rAll * 0.1)
save_ckpt(qnetwork) # save model
plt.plot(all_episode_reward)
if not os.path.exists('image'):
os.makedirs('image')
plt.savefig(os.path.join('image', '_'.join([alg_name, env_id])))
if args.test:
load_ckpt(qnetwork) # load model
for i in range(num_episodes):
## Reset environment and get first new observation
s = env.reset() # observation is state, integer 0 ~ 15
rAll = 0
if render: env.render()
for j in range(99): # step index, maximum step is 99
## Choose an action by greedily (with e chance of random action) from the Q-network
allQ = qnetwork(np.asarray([to_one_hot(s, 16)], dtype=np.float32)).numpy()
a = np.argmax(allQ, 1) # no epsilon, only greedy for testing
## Get new state and reward from environment
s1, r, d, _ = env.step(a[0])
rAll += r
s = s1
if render: env.render()
## Reduce chance of random action if an episode is done.
if d: break
print('Testing | Episode: {}/{} | Episode Reward: {:.4f} | Running Time: {:.4f}' \
.format(i, num_episodes, rAll, time.time() - t0))
rList.append(rAll)
print("正确率: " + str(sum(rList) / num_episodes * 100) + "%")
3.2.2 实验结果
DQN采用了神经网络替代表格，所以最终保存的是神经网络结构和参数，只需输入新的状态就能得到相应的移动方向。
训练阶段迭代10000次，各代累积奖励曲线如下：
三次测试的正确率如下：
