Shelf four · Agents, tools & reinforcement
Reinforcement Learning
Kitchen table
Like training a puppy. You cannot explain 'sit' in words. The puppy tries things, and when it happens to sit, it gets a treat. Slowly it works out which actions earn treats. Machines can learn the same way: try, score, adjust, try again – millions of times, usually in a pretend world where mistakes are free. And exactly like the puppy, it learns what you actually reward, not what you meant. Reward 'looking sorry' and you get a champion at looking sorry.
Whiteboard
Agent, environment, reward, policy. Credit assignment over long horizons is brutal, which of a thousand moves won the game? Sample efficiency is poor, and simulation quality decides everything, because the policy will exploit any gap between the sim and reality. Reward hacking is not an anecdote, it is the default outcome of a misspecified objective. Assume the policy finds the cheapest path to the number. Where the sim is faithful and the score is clean, the results still look like magic.
Boardroom
The fit is narrow and real: a clear score, cheap practice, tolerable mistakes – pricing, routing, scheduling, bidding, recommendations. Where practice is expensive or errors are irreversible, this is the wrong tool, whatever the vendor deck says. The board-level question for any RL proposal is about the reward: what exactly is being maximised, and what is the ugliest behaviour that would still make that number go up? If nobody in the room can answer, the system will answer for you, in production.
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