Update README with new policy weights location

[dvij542]

No description provided.

[gemini-code-assist]

Summary of Changes

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This pull request updates the README.md file to correct an outdated file path. This ensures that users looking for pre-trained Sim2Real RL policy weights are directed to the accurate location within the repository, improving the clarity and usability of the documentation.

Highlights

• Documentation Update: Corrected the file path for pre-trained Sim2Real RL policy weights in the README.md to reflect their new location at Sim2Real/deploy/policies/.

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Changelog

• Sim2Real/README.md
  • Updated the link to the policy weights directory.

Activity

• No specific review comments or activity have been recorded for this pull request yet.

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[gemini-code-assist]

Code Review

This pull request updates the location of pre-trained policy weights in the README.md file. The change is correct and aligns with the repository's structure. I have added one comment suggesting the use of a relative path instead of an absolute URL to improve the documentation's maintainability. The other change, adding a newline at the end of the file, is a good practice.

_{Note: Security Review has been skipped due to the limited scope of the PR.}

[gemini-code-assist]

For better maintainability and portability of the documentation, it's recommended to use relative links for files within the same repository. Absolute URLs are tied to a specific branch (e.g., main) and might not work correctly in forks or other branches.

Suggested change

	Train Sim2Real RL policy for the tasks. The trained policy weights for most tasks are already in the [Sim2Real/deploy/policies/](https://github.com/GenRobo/DreamControl/tree/main/Sim2Real/deploy/policies/) folder. You may skip this step if you want to directly use these trained policies for inference.
	Train Sim2Real RL policy for the tasks. The trained policy weights for most tasks are already in the [Sim2Real/deploy/policies/](./deploy/policies/) folder. You may skip this step if you want to directly use these trained policies for inference.