Introduction
Introduction
Project Overview
The goal of this project is to leverage Large Language Models (LLMs) to autonomously learn and optimize behavior-control policies for mice through reinforcement learning (RL). By utilizing a closed-loop system, the LLM will iteratively generate, evaluate, and refine its strategies to influence the mouse's location and behavior within a controlled environment.
The system is equipped with the following tools:
Food Dispenser - A reward system to reinforce desired behaviors.
Speakers - Audio stimuli to signal or condition specific responses.
LED Lights - Visual cues to guide or influence the mouse's movement and focus.
Camera Feed - A real-time video stream to monitor and analyze the mouse's actions.
Using these tools, the LLM will start by controlling simple behaviors, such as directing the mouse to a specific location within its cage. As the system progresses, the complexity of the behaviors will increase, moving towards more intricate tasks like running on a wheel, spinning, or following a series of commands.
Self-Learning Framework
The project is designed as a closed-loop system. The LLM will have access to a ranked list of behaviors to teach the mouse, but it must autonomously generate, execute, and evaluate behavior-control policies. Through continuous self-iteration, the LLM will refine its strategies until it achieves reliable control of the mouse's behavior.
This process follows a reinforcement learning cycle:
Policy Generation: The LLM creates a policy using the available tools (e.g., triggering the food dispenser or lights).
Execution: The policy is applied to the environment.
Observation: Camera feeds provide real-time feedback on the mouse's behavior.
Evaluation: The system evaluates the policy's effectiveness based on predefined metrics.
Iteration: The LLM adjusts and refines the policy to improve outcomes in subsequent cycles.
Streaming Policies
Given the dynamic nature of the policies the LLM generates, it is essential to have a mechanism for streaming and logging the evolving strategies. This will allow developers and researchers to:
Monitor the LLM's reasoning process and decision-making.
Analyze the policies generated over time.
Identify trends, challenges, and optimization opportunities.
Project Significance
This project bridges the gap between AI and behavioral neuroscience by demonstrating the capacity of LLMs to learn and adapt in a real-world environment. It offers a scalable framework for automating behavioral conditioning, with applications ranging from neuroscience research to automated animal training systems. Additionally, it highlights the potential of combining reinforcement learning techniques with LLMs to tackle increasingly complex, real-world control problems.
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