When I hear AI agents, I hear RL (reinforcement learning), not LLMs. RL may not be having the moment that LLMs are, but the progress in recent years is incredi

When I hear AI agents, I hear RL (reinforcement learning), not LLMs. RL may not be having the moment that LLMs are, but the progress in recent years is incredible and they are absolutely solving real world problems. I was just listening to a podcast about using an RL algorithm to enhance the plasma containment system in a fusion reactor, and the results were incredible. It quickly learned a policy that was competitive with the existing system that had been hand built over many years at a cost of millions. It even provided some new insights and surprises. RL is SOTA in robotics control, and some new algorithms like Dreamer V3 can generalize in realtime without millions of samples. It's has already grown way beyond solving ATARI games and is in many cases being used to train LLMs and other generative AI. There is a good amount of research going into combining LLMs with RL for decision making, it is a powerful combination. LLMs help with high level reasoning and goal setting, and of course provide a smooth interface for interacting with humans and with other agents. LLMs also contain much of the collective knowledge of humanity, which is very useful for training agents to do things. If you want to train a robot to make a sandwich it's helpful to know things, like what is a sandwich, and that it is necessary to move, get bread, etc. These feedback loop LLM agent projects are kind of misguided IMO. AI agents are real and useful and progressing fast, but we need to combine more tools than just LLM to build effective systems. Personally, I am using LLMs quite effectively for ecommerce: classifying messages, drafting responses, handling simple requests like order cancellation. All kinds of glue stuff that used to be painful is now automated and easy. I could go on.