Reinforcement Learning
Reinforcement Learning (RL) is a learning paradigm where an AI agent learns through trial and error to make optimal decisions in an environment. The agent receives reward signals for good actions and penalties for bad ones. RL is the foundation for RLHF (Reinforcement Learning from Human Feedback), which makes LLMs like ChatGPT and Claude more human-like.
Why does this matter?
Reinforcement learning optimizes decision processes under uncertainty: dynamic pricing, logistics routing, resource planning. For mid-sized businesses, RL is particularly relevant for repetitive decisions with many variables where human intuition reaches its limits — such as production planning or inventory management.
How IJONIS uses this
We deploy RL specifically where classical optimization fails: in dynamic environments with many variables. For most business applications, we first recommend simpler ML methods. RL is used for process optimization, routing problems, and adaptive decision logic in AI agents.