Migrating Actor Critic Method example to Keras 3 (TF-Only)

[sitamgithub-MSIT]

This PR changes the Actor Critic Method example to Keras 3.0 [TF Only Backend].

For example, here is the notebook link provided:
https://colab.research.google.com/drive/15pFWOBmb0bVPv7qmAFC2zjMzUiQeAbuT?usp=sharing

cc: @fchollet @divyashreepathihalli

The following describes the Git difference for the changed files:

Changes:

diff --git a/examples/rl/actor_critic_cartpole.py b/examples/rl/actor_critic_cartpole.py
index 14787341..e34000d3 100644
--- a/examples/rl/actor_critic_cartpole.py
+++ b/examples/rl/actor_critic_cartpole.py
@@ -39,11 +39,15 @@ remains upright. The agent, therefore, must learn to keep the pole from falling
 ## Setup
 """
 
+import os
+
+os.environ["KERAS_BACKEND"] = "tensorflow"
 import gym
 import numpy as np
+import keras
+from keras import ops
+from keras import layers
 import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import layers
 
 # Configuration parameters for the whole setup
 seed = 42
@@ -97,8 +101,8 @@ while True:  # Run until solved
             # env.render(); Adding this line would show the attempts
             # of the agent in a pop up window.
 
-            state = tf.convert_to_tensor(state)
-            state = tf.expand_dims(state, 0)
+            state = ops.convert_to_tensor(state)
+            state = ops.expand_dims(state, 0)
 
             # Predict action probabilities and estimated future rewards
             # from environment state
@@ -107,7 +111,7 @@ while True:  # Run until solved
 
             # Sample action from action probability distribution
             action = np.random.choice(num_actions, p=np.squeeze(action_probs))
-            action_probs_history.append(tf.math.log(action_probs[0, action]))
+            action_probs_history.append(ops.log(action_probs[0, action]))
 
             # Apply the sampled action in our environment
             state, reward, done, _ = env.step(action)
@@ -151,7 +155,7 @@ while True:  # Run until solved
             # The critic must be updated so that it predicts a better estimate of
             # the future rewards.
             critic_losses.append(
-                huber_loss(tf.expand_dims(value, 0), tf.expand_dims(ret, 0))
+                huber_loss(ops.expand_dims(value, 0), ops.expand_dims(ret, 0))
             )
 
         # Backpropagation
(END)

[divyashreepathihalli]

LGTM! please add the ipynb and .md file

[sitamgithub-MSIT]

LGTM! please add the ipynb and .md file

Every file is added. It appears to be prepared for merging. @fchollet

[fchollet]

Nice, thank you!