What is the difference between reinforcement learning and deep learning? DL and RL are both sub-fields of machine learning systems that learn autonomously. Deep learning uses data to train a model to make predictions from new data.
Deep Learning God Yann LeCun
Here, the goal is usually to train a computer to do as well or better than a human at some task such as image recognition, speech recognition and translation. And other skills that a human is considered to be good at, without human involvement.
Michael Littman, Ai innovator considers Deep Learning to be a mechanism, whereas Reinforcement Learning is method.
However, unlike recognizing an image or speech problems in the financial markets may be different from typical deep learning applications, for example, select a stock that is likely to perform well in some future period.
Reinforcement learning (RL), on the other hand, utilizes a software agent to make observations and takes actions within an environment. And in return it receives rewards and its objective is to learn to act in a way that will maximize its expected long-term rewards. It offers the advantage of solving the complex sequential decision-making problems based on learning from the previous knowledge since. As a result, it provides a dynamic learning against the changing environment and no need to pre-determined model of environment.
As a result, the difference is that DL is learning from a training set and then applying that learning to a new data set. While reinforcement learning is dynamically learning by adjusting actions based in continuous feedback. To maximize a reward, which makes it more suitable for financial applications.
However, DL and RL aren’t mutually exclusive. In fact, one might use deep learning in a reinforcement learning system. AKA, deep reinforcement learning.
In conclusion, one should use them together, not consider them rivals.
