MMGR-RL Project

# MMGR-RL Project ## Description This project investigates the hypothesis that integrating multimodal generative reasoning (MMGR) frameworks with reinforcement learning (RL) can enhance the decision-making capabilities of autonomous agents in complex environments. ## Research Hypothesis Integrating MMGR with RL could significantly improve autonomous agents' decision-making based on complex, multi-faceted inputs, addressing current models' limitations in nuanced understanding during real-world navigation. ## Implementation Approach - Develop a reinforcement learning framework incorporating MMGR-enhanced generative models. - Conduct experiments in AI2-THOR where agents complete tasks requiring spatial understanding and reasoning. - Compare performance against traditional RL models. ## Setup Instructions 1. Clone the repository. 2. Install the required packages: ```bash pip install -r requirements.txt ``` 3. Set up AI2-THOR environment as per their [installation guide](https://ai2thor.allenai.org/documentation/installation). ## Usage Examples To train an agent: ```bash python src/train.py ``` To evaluate an agent: ```bash python src/evaluate.py ``` ## Expected Results We expect the MMGR-enhanced agents to outperform traditional RL agents in tasks requiring complex reasoning and decision-making. ## References - [MMGR: Multi-Modal Generative Reasoning](http://arxiv.org/abs/2512.14691v1)