ValueError: No gradients provided for any variable tensorflow 2.0 - tensorflow

Question

I am using tensorflow 2.0 and trying to make a actor critic algorithm to play the game of cartpole. I have done everything right but getting the following error: ValueError: No gradients provided for any variable: ['dense/kernel:0', 'dense/bias:0', 'dense_1/kernel:0', 'dense_1/bias:0'].
Please help me out
Here is my code:
import gym
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
MAX_EPISODES = 2000
GAMMA = 0.9
LR_A = 0.001
LR_C = 0.01
env = gym.make("CartPole-v0")
N_ACTIONS = env.action_space.n
N_FEATURES = 4
def make_actor(n_features, n_actions):
inputs = tf.keras.Input(shape=[n_features])
hidden = tf.keras.layers.Dense(20, activation=tf.nn.relu)(inputs)
dist = tf.keras.layers.Dense(n_actions, activation=tf.nn.softmax)(hidden)
model = tf.keras.Model(inputs=inputs, outputs=dist)
return model
def make_critic(n_features):
inputs = tf.keras.Input(shape=[n_features])
hidden = tf.keras.layers.Dense(20, activation=tf.nn.relu)(inputs)
value = tf.keras.layers.Dense(1)(hidden)
model = tf.keras.Model(inputs=inputs, outputs=value)
return model
actor = make_actor(N_FEATURES, N_ACTIONS)
critic = make_critic(N_FEATURES)
actor.summary()
critic.summary()
actor_optimizer = tf.keras.optimizers.Adam(LR_A)
critic_optimizer = tf.keras.optimizers.Adam(LR_C)
def loss_actor(s, a, td_error):
dist = actor(s.reshape(1, 4)).numpy()
log_prob = np.log(dist[0, a])
exp_v = np.mean(log_prob * td_error)
return tf.multiply(exp_v, -1)
def loss_critic(s, s_, r, gamma):
s, s_ = s[np.newaxis, :], s_[np.newaxis, :]
v = critic(s)
v_ = critic(s_)
td_error = r + gamma * v_ - v
return tf.multiply(td_error, 1)
def train(max_episodes):
for episode in range(max_episodes):
s = env.reset().astype(np.float32)
t = 0
track_r = []
while True:
dist = actor(s.reshape(1, 4)).numpy()
a = np.random.choice(range(N_ACTIONS), p=dist.ravel())
s_, r, done, info = env.step(a)
s_ = s_.astype(np.float32)
if done: r=-20
track_r.append(r)
with tf.GradientTape() as cri_tape, tf.GradientTape() as act_tape:
td_error = loss_critic(s, s_, r, GAMMA)
gradient = cri_tape.gradient(td_error, critic.trainable_variables)
critic_optimizer.apply_gradients(zip(gradient,critic.trainable_variables))
with tf.GradientTape() as act_tape:
neg_exp_v = loss_actor(s, a, td_error.numpy())
gradient = act_tape.gradient(neg_exp_v, critic.trainable_variables)
actor_optimizer.apply_gradients(zip(gradient, actor.trainable_variables))
s = s_
t += 1
if done:
print("Episode:{} Steps:{}".format(episode+1, t))
train(MAX_EPISODES)
The error is on line 69:actor_optimizer.apply_gradients(zip(gradient, actor.trainable_variables))
When I tried to print out the gradients for the actor the result was None.
I am really not getting where the problem is.