Have you ever learned something by trial and error?
Like playing a video game and getting better with time?
Thatโs the basic idea behind reinforcement learning.
Itโs a way to learn by doing.
You try something. If it works, great! If not, you adjust.
This is how AI agents learn to play games.
They donโt know the best move at first. But over time, they learn from feedback.
Imagine a robot in a maze.
Every time it hits a wall, it gets -1 point.
When it finds the exit, it gets +10 points.
At first, the robot moves randomly. But after many tries, it learns the best path.
Thatโs reinforcement learning!
Reinforcement learning is popular in AI for games.
It helped DeepMind beat top players in Go and StarCraft.
Games are great for RL because:
No-regret learning is a smart way to make decisions.
In simple words: you want to avoid looking back and saying, โI should have done something else every time.โ
Over time, a no-regret learner does almost as well as the best fixed strategy in hindsight.
You try different pizza places for lunch.
Some are good. Some are bad.
Each day, you pick based on past experience.
Eventually, you mostly go to the best one.
You may regret some choices. But overall, your regret is small.
No-regret algorithms are used when players donโt know what others will do.
Each player updates their strategy based on outcomes.
If all players use no-regret learning, the game may reach an equilibrium.
This is helpful when computing Nash equilibrium is too hard.
Instead of solving the game directly, players learn it!
So next time you try something new and improve over time, congrats โ you're doing reinforcement learning! ๐