human
I am working on the file "RL-Notebooks/Part1_2.ipynb".
The first task is:
The Policy-Based Agent
The code below established our simple neural agent. It takes as input the current state, and returns an action. This allows the agent to take actions which are conditioned on the state of the environment, a critical step toward being able to solve full RL problems. The agent uses a single set of weights, within which each value is an estimate of the value of the return from choosing a particular arm given a bandit. We use a policy gradient method to update the agent by moving the value for the selected action toward the recieved reward.
Can you write Python code for it?
assistant
#Here is the Python code you requested.

class agent():
    def __init__(self, lr, s_size,a_size):
        #These lines established the feed-forward part of the network. The agent takes a state and produces an action.
        self.state_in= tf.placeholder(shape=[1],dtype=tf.int32)
        state_in_OH = slim.one_hot_encoding(self.state_in,s_size)
        output = slim.fully_connected(state_in_OH,a_size,\
            biases_initializer=None,activation_fn=tf.nn.sigmoid,weights_initializer=tf.ones_initializer())
        self.output = tf.reshape(output,[-1])
        self.chosen_action = tf.argmax(self.output,0)

        #The next six lines establish the training proceedure. We feed the reward and chosen action into the network
        #to compute the loss, and use it to update the network.
        self.reward_holder = tf.placeholder(shape=[1],dtype=tf.float32)
        self.action_holder = tf.placeholder(shape=[1],dtype=tf.int32)
        self.responsible_weight = tf.slice(self.output,self.action_holder,[1])
        self.loss = -(tf.log(self.responsible_weight)*self.reward_holder)
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=lr)
        self.update = optimizer.minimize(self.loss)

tf.reset_default_graph() #Clear the Tensorflow graph.

cBandit = contextual_bandit() #Load the bandits.
myAgent = agent(lr=0.001,s_size=cBandit.num_bandits,a_size=cBandit.num_actions) #Load the agent.
weights = tf.trainable_variables()[0] #The weights we will evaluate to look into the network.

total_episodes = 10000 #Set total number of episodes to train agent on.
total_reward = np.zeros([cBandit.num_bandits,cBandit.num_actions]) #Set scoreboard for bandits to 0.
e = 0.1 #Set the chance of taking a random action.

init = tf.initialize_all_variables()

# Launch the tensorflow graph
with tf.Session() as sess:
    sess.run(init)
    i = 0
    while i < total_episodes:
        s = cBandit.getBandit() #Get a state from the environment.
        
        #Choose either a random action or one from our network.
        if np.random.rand(1) < e:
            action =...