Let me give you an example. Imagine your RL agent is playing Pac-Man, but it’s never played before. It doesn’t know which direction to go or how many points it can get for eating a pellet. So, the first few times it plays, it might try going left instead of right just to see what happens (exploration). But if it keeps doing that and never eats any pellets, it won’t learn anything useful (and will probably lose all its lives).
On the other hand, let’s say your RL agent has been playing Pac-Man for a while now and knows exactly where to go for maximum points. It might stick with that strategy every time it plays (exploitation), but if there’s a new maze or some other change in the game, it won’t be able to adapt as quickly (and will probably lose all its lives).
So, how does our RL agent find this balance between exploration and exploitation? Well, one popular method is called Q-learning. Basically, every time your RL agent takes an action in the game, it gets a reward based on what happens next (like eating a pellet or getting eaten by a ghost). It then updates its “Q table” to remember how good that action was in that particular situation.
For example, let’s say our RL agent is at the top of the maze and it can go left or right. If it goes left and gets a pellet (reward = +10), then its Q value for going left from that position will increase by some amount (like 0.5). But if it goes left and gets eaten by a ghost (-100), then its Q value for going left from that position will decrease by the same amount.
Over time, our RL agent will learn which actions are best in each situation based on these rewards. And when it’s not sure what to do (like at the beginning of the game or in a new maze), it can use a technique called epsilon-greedy exploration to try out some random moves as well.
There you go! The exploration vs exploitation trade-off in Reinforcement Learning is all about finding that sweet spot between trying new things and sticking with what works. And Q-learning is just one way to do it (but there are many others).