Fix gymnasium compatibility in actor_critic_cartpole.py

[samudraneel05]

Fixes keras-team/keras#21092
Link to the issue here

Some of the lines of code in the actor-critic implementation in the reinforcement learning code examples had some bugs related to deprecation of the libraries and some few tweaks needed.

• Update dCartPole-v0 to CartPole-v1 to remove deprecation error
• Fix env.reset() API to handle new tuple return
• Fix env.step() API for terminated/truncated flags
• Update tensor conversion to use tf.convert_to_tensor
• Fix state/obs variable handling

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[gemini-code-assist]

Summary of Changes

Hello @samudraneel05, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request modernizes the actor_critic_cartpole.py reinforcement learning example by updating its dependencies and API usage to align with recent changes in the gymnasium library and TensorFlow. The primary goal is to ensure the example remains functional and demonstrates correct usage of the latest environment interaction patterns, resolving several deprecation-related issues.

Highlights

• Gymnasium Compatibility: Updated the actor_critic_cartpole.py example to be compatible with the latest gymnasium API, addressing deprecation errors and changes in environment interaction.
• Environment Update: Migrated the CartPole environment from CartPole-v0 to the newer CartPole-v1 version, reflecting current best practices and removing deprecation warnings.
• API Modernization: Adapted env.reset() and env.step() calls to handle the new tuple return formats, including separate terminated and truncated flags for episode termination.
• Tensor Conversion: Switched tensor conversion from ops.convert_to_tensor to tf.convert_to_tensor for consistency and updated variable handling from state to obs after environment reset.

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[gemini-code-assist]

Code Review

This pull request successfully updates the actor_critic_cartpole.py example to be compatible with the latest Gymnasium API, addressing deprecations and API changes. The updates to use CartPole-v1 and correctly handle the return values from env.reset() and env.step() are well-implemented. I have one suggestion to improve the code's consistency and adherence to Keras 3 best practices by using keras.ops for tensor operations instead of direct TensorFlow calls. This will enhance the backend-agnostic nature of the example.

[gemini-code-assist]

Code Review

This pull request effectively addresses the compatibility issues with the gymnasium library in the actor-critic example. The changes correctly update the environment from CartPole-v0 to CartPole-v1, switch the import from gym to gymnasium, and adapt the code to the new API for env.reset() and env.step(). The handling of the terminated and truncated flags is also correct. Furthermore, the refactoring of variable names from state to obs for environment observations significantly improves the readability and clarity of the training loop. The changes are consistent across the Python script, Jupyter notebook, and Markdown file. Overall, this is a solid contribution that fixes the reported issue and improves code quality.

[samudraneel05]

@sachinprasadhs @sonali-kumari1 open for review, have updated the ipynb and markdown files.
Live colab notebook here