Reinforcement learning, RL, is seen as one of the basic machine learning paradigms, next to supervised learning and unsupervised learning. RL is all about decisions: delivering the right decisions or at least learning from them.
Reinforcement learning, RL, is seen as one of the basic machine learning paradigms, next to supervised learning and unsupervised learning. RL is all about decisions: delivering the right decisions or at least learning from them.
Imagine you have a simulated environment, like the stock market for example. What happens if you impose this or that regulation does it have a positive or negative effect? The whole point is being able to change course if something negative happen, so called _negative reinforcement_ or if it's a positive outcome, to keep building on that, so called_positive reinforcement_.
Imagine you have a simulated environment such as the stock market. What happens if you impose a given regulation. Does it have a positive or negative effect? If something negative happens, you need to take this _negative reinforcement_, learn from it, and change course. If it's a positive outcome, you need to build on that_positive reinforcement_.
Jen Looper
4 years ago