Reinforcement learning is a discipline within Machine Learning where an agent (RL agent) learns its actions (that sometimes includes inaction) through real-time interactions with its environment. Any action the agent takes impacts its environment. As a result, the environment goes through a transition and offers a reward. These rewards act as feedback signals that enable the RL agent to fine-tune its actions. With every training episode, the RL agent adjusts its action policy to arrive at a sequence that will maximize its rewards.
In reinforcement learning, coming up with the right reward system is often challenging. Many times, the rewards get delayed for long. Imagine an RL agent for Chess. This agent may only be rewarded after it defeats its opponent. In an ideal scenario, these involve a lot of training episodes and thousands of moves before the RL agent learns the winning combo.
This is where reinforcement learning from human feedback (RLHF) may be useful. In this concept, the RL agent’s learning is facilitated with human feedback. By involving humans in the training process, we account for all those elements that can be quantified or measured effectively for the reward system.
One of the greatest advantages of a machine learning system is its ability to scale. But when you involve humans in the training process, it becomes a bottleneck for scalability. This is why most RLHF systems use a combination of human and automated reward signals. The primary feedback for the RL agent comes from the computational system. The human supervisor merely compliments the computational system by occasionally signaling a punishment or additional reward. They may also offer other input data to train the reward system.
Examples of RLHF systems
Autonomous vehicles
RLHF can be effectively used in training self-driving cars and vehicles. For example, human experts can guide these systems to handle complex traffic situations and make safe driving decisions. Humans can offer training demonstrations and feedback on typical driving behaviors. The RL agent can use this feedback to improvise on its driving actions over a period of time.
Gaming
RLHF is employed in gaming to train AI agents to play complex games. Human gamers can demonstrate playing strategies to optimize the agents’ actions or provide feedback for the agent to correct their wrong actions. Over time, the RL agent uses this feedback to improve its decision-making abilities and perform better at the gameplay.
Robotics
RLHF helps robots learn from human experts, where humans can suggest corrective actions in complex robotic tasks. For example, suppose a robot is learning to manipulate objects. In that case, it can use human feedback and demonstrations on the right approach to grasp an item and improve its performance and actions.
Dialogue systems
In training conversational systems like chatbots, RLHF may be employed with humans providing sample dialogues and conversations to enable learning. The RLHF agent learns from these examples and feedback and strives to generate more coherent, relevant, and meaningful responses. Human expertise will also be used to correct the agents’ responses, thus enhancing their conversational capabilities.
Natural language processing
RLHF can train agents in NLP tasks such as text generation, language translation, Q&A, etc. Human experts can help train the RL agent to produce more accurate and meaningful outputs by offering relevant feedback on the agent’s performance.
As evident from the above examples, human feedback plays a significant role and serves as expert guidance for the RL agents to learn the process more effectively. Thus, Reinforcement learning from human feedback seals the gap between what the agent already knows and the desired knowledge for the agent to perform faster and more accurately.
Related terms
Machine learning Natural language processing Reinforcement learning
