First lets discuss what is Reinforcement Learning?:
In the machine learning technique known as reinforcement learning, an agent gains decision-making skills by interacting with its surroundings and getting feedback in the form of incentives or penalties. The objective is for the agent to gradually develop a policy that optimizes the cumulative reward. RL learns from the results of its actions rather than labeled data, which is necessary for supervised learning.
Essential Ideas:
Agent: The one making the decisions (like an AI model).
Environment: The area where the agent functions.
Actions: Decisions the agent takes.
Rewards are comments that let the agent know how well or poorly an action went.
Policy: The method by which the agent chooses what to do next.
To combine Generative ai with Reinforcement learning you need to follow the steps:
- Get the model pre-trained: Start with a generative model that has already been trained (like GPT) using a large dataset in a conventional manner.
- Describe the function of rewards: Make a function that assigns a score to the model's output according on how closely it matches your intended result. A rule-based system or user input may be used in this situation.
- Use Policy Optimization: Change the model's weights in response to feedback by using an RL method (such as Proximal Policy Optimization, or PPO). This aids the model in determining the desired outcomes.
- Iterative Training: In order to optimize the cumulative reward, the model iteratively produces new outputs, gets feedback, and modifies its weights.
Basic workflow of above steps :
Application in the Real World: Reinforcement Learning from Human Feedback (RLHF)
RLHF is a real-world example of combining RL and GPT, in which generated responses are evaluated by human judges. A reward model that evaluates the outputs is trained using the feedback, directing the training process to conform to human preferences.
Obstacles & Things to Think About:
- Reward Design: Developing a successful reward system is essential and frequently the most challenging aspect.
- Stability: RL training big models can be unstable, necessitating careful hyperparameter adjustment.
- Computational Resources: RL can be resource-intensive, particularly when working with huge models like GPT.
Hence these are the things and strategies you need to remember when integrating generative ai with reinforcement learning.
