GPRO - Feature Addition

[Soham4001A]

Description

This PR introduces Generalized Policy Reward Optimization (GRPO) as a new feature in stable-baselines3-contrib. GRPO extends Proximal Policy Optimization (PPO) by incorporating:
• Sub-step sampling per macro step, allowing multiple forward passes before environment transitions.
• Customizable reward scaling, enabling users to pass their own scaling functions or use the default tanh-based normalization.
• Better adaptability in reinforcement learning (RL) tasks, particularly for tracking and dynamic environments.

GRPO allows agents to explore action spaces more efficiently and refine their policy updates through multiple evaluations per time step.

Context

(DLR-RM/stable-baselines3#2076

• [x ] I have raised an issue to propose this change (required)

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• [x ] New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Documentation (update in the documentation)

Checklist:

• [x ] I've read the CONTRIBUTION guide (required)
• [x ] The functionality/performance matches that of the source (required for new training algorithms or training-related features).
• [x ] I have updated the tests accordingly (required for a bug fix or a new feature).
• [x ] I have included an example of using the feature (required for new features).
• [x ] I have included baseline results (required for new training algorithms or training-related features).
• I have updated the documentation accordingly.
• [ x] I have updated the changelog accordingly (required).
• [x ] I have reformatted the code using make format (required)
• [ x] I have checked the codestyle using make check-codestyle and make lint (required)
• [ x] I have ensured make pytest and make type both pass. (required)