Does reinforcement learning come under deep learning?

Difference between deep learning and reinforcement learning The difference between them is that deep learning 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.

Do deep reinforcement learning algorithms really learn to navigate?

At best, we can say that DRL-based algorithms learn to navigate in the exact same environment, rather than general technique of navigation which is what classical mapping and path planning provide.

Is deep reinforcement learning useful?

The use of deep learning is most useful in problems with high-dimensional state space. This means, that with deep learning, Reinforcement Learning is able to solve more complicated tasks with lower prior knowledge because of its ability to learn different levels of abstractions from data.

How to improve generalization in deep reinforcement learning?

A very interesting paper called “ A Simple Randomization Technique for Generalization in Deep Reinforcement Learning ” presented a nice method to improve generalization over the standard regularization shown before. They suggest to add a convolutional layer just between the input image and the neural network policy, that transforms the input image.

What is deep reinforcement learning (deep RL)?

Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error.

Can AI generalization in deep RL be tested?

In the past year, OpenAI has released two benchmarks for generalization in deep RL: the Retro contest, which tests if a game-playing AI generalizes to previously unseen levels of the same game (Sonic The Hedgehog), and CoinRun, a new game environment that tests agents’ ability to generalize to new levels created using a procedural generator.

What is an example of a reinforcement learning environment?

An example of such an environment is CoinRun, introduced by OpenAI in the paper “Quantifying Generalization in Reinforcement Learning”. This environment can produce a large variety of levels with different layouts and visual appearance, and thus serves as a nice benchmark for generalization.