rl

I'm so confused. I just found a small body of literature applying LLMs to reinforcement learning type tasks, exploring the use of LLMs for "autonomous decision making."

I guess people are building more LLM agent systems, and we ought to understand them and what makes them better / worse at what they do.

But I still feel like LLMs are fundamentally not suited to decision making tasks. They don't weigh options and decide. At best, you could say they interpolate what a reasonable choice might look like based on the examples of people making choices in their training data.

That's... really not the same thing! Like, not at all. It's impressive that this sometimes works, but this seems very silly to me when we could be using actual RL systems that really are making informed decisions from experience, with mathematical rigor to estimate the quality of those choices.

#ai #llm #rl

Reinforcement Learning is AI for playing games, planning actions, and doing things in the real world. It's extremely powerful, but also a fun and interesting topic to learn. I'm teaching myself now.

I was always intimidated by all the esoteric jargon and math in RL. The classic Sutton and Barto text clears that up nicely, though. It's one of the best text books I have ever studied, and it reveals RL is much less complex than it sounds.

I think the excessive mathiness comes from the question "is this policy optimal?" which is a natural place to start, and has been fruitful for the field.

Except, in practice it's rare we can find optimal policies for practical problems. Also, our mathematical formalisms often don't fit, which is why there are so many variations based on what information is available, and what you want to focus on. This produces the tangled mess of names and notations, but really it's just the same idea from subtly different angles.

#rl #ai #programming