Stable Panther

Everyone’s talking about reinforcement learning like it’s the future of AI. It’s not. Here’s what actually happens when you try to make RL work in the real world.

Why RL Fails (Most of the Time)

The sample efficiency problem. RL needs millions of samples to learn anything useful. In the real world, you can’t afford to let your robot crash into walls a million times just to learn how to walk.

Reward hacking is everywhere. Your agent will find ways to maximize reward that you never intended. Reward for “distance traveled”? Your agent will learn to spin in circles really fast. Reward for “not dying”? Your agent will learn to never move.

The exploration-exploitation dilemma is unsolvable. You need to explore to learn, but you need to exploit to perform. The balance is impossible to get right, and your agent will either learn nothing or perform terribly.

The Three Rules of RL

Rule 1: If you can solve it with supervised learning, do that instead. RL is for problems where you don’t have labeled data and can’t easily get it. If you can collect training data, use supervised learning.

Rule 2: Start with the simplest algorithm that might work. Don’t use PPO or SAC for a simple grid world. Use Q-learning or SARSA first. You can always upgrade later.

Rule 3: Your reward function will be wrong. No matter how carefully you design it, your agent will find ways to game it. Plan for this. Build in safety constraints and monitoring.

When RL Actually Works

Games with clear rules. Chess, Go, video games - these work because the rules are well-defined and you can simulate millions of games.

Robotics in controlled environments. Industrial robots, warehouse automation - these work because the environment is predictable and you can afford to let the robot make mistakes.

Trading algorithms. Financial markets are complex, but you can simulate them and the reward function (profit) is clear.

The Hard Truth

RL is not the future of AI. It’s a useful tool for specific problems, but it’s not going to solve general intelligence or replace supervised learning.

Most RL papers are misleading. They show the best case scenario, not the reality of trying to make RL work in production.

RL is expensive and slow. If you need something to work reliably in production, RL is probably not the right choice.

Still think you need RL? Contact us and let’s talk about whether there’s a simpler solution.