DDPG-training.py

X,A,R,X2,D = replay_buffer.sample(batch_size)
X = np.asarray(X,dtype=np.float32)
A = np.asarray(A,dtype=np.float32)
R = np.asarray(R,dtype=np.float32)
X2 = np.asarray(X2,dtype=np.float32)
D = np.asarray(D,dtype=np.float32)
Xten=tf.convert_to_tensor(X)   

#Actor optimization   
with tf.GradientTape() as tape2:
  Aprime = action_max * mu.predict_on_batch(X)
  temp = tf.keras.layers.concatenate([Xten,Aprime],axis=1)
  Q = q_mu.predict_on_batch(temp)
  mu_loss =  -tf.reduce_mean(Q)
  grads_mu = tape2.gradient(mu_loss,mu.trainable_variables)
mu_losses.append(mu_loss)
mu_optimizer.apply_gradients(zip(grads_mu, mu.trainable_variables))



#Critic Optimization
with tf.GradientTape() as tape:
  next_a = action_max * mu_target.predict_on_batch(X2)
  temp = np.concatenate((X2,next_a),axis=1)
  q_target = R + gamma * (1 - D) * q_mu_target.predict_on_batch(temp)
  temp2 = np.concatenate((X,A),axis=1)
  qvals = q_mu.predict_on_batch(temp2) 
  q_loss = tf.reduce_mean((qvals - q_target)**2)
  grads_q = tape.gradient(q_loss,q_mu.trainable_variables)
q_optimizer.apply_gradients(zip(grads_q, q_mu.trainable_variables))
q_losses.append(q_loss)