Reinforcement Learning (RL): A Comprehensive Guide
Reinforcement learning (RL) is a type of machine learning that allows algorithms to learn from their environments by collecting data and training policies to maximize rewards. Unlike supervised and unsupervised learning, RL algorithms autonomously gather data to train models rather than being provided with training data. In this article, we will delve into the concept of RL, its sub-classes, and a specific algorithm called Adversarial Inverse Reinforcement Learning (AIRL).
What is Reinforcement Learning?
RL is one of the three fundamental machine learning approaches, alongside supervised and unsupervised learning. The key difference between RL and other approaches is that RL algorithms collect data from their environments to train policies rather than being given training data. These algorithms determine rewards by maximizing a reward function obtained from their environment.
RL Sub-Classes: Imitation Learning (IL) and Deep Reinforcement Learning (DRL)
While RL algorithms autonomously gather data, IL algorithms attempt to imitate an expert’s actions. Two important sub-classes of IL are Behavior Cloning (BC) and Inverse Reinforcement Learning (IRL). BC algorithms directly mimic an expert’s behavior, whereas IRL methods discover a reward function from demonstrated ‘expert’ actions.
Deep Reinforcement Learning (DRL): A Brief Survey
DRL is a sub-class of RL that uses deep neural networks to store policies instead of tables or other methods. DRL has gained popularity in recent years due to its ability to handle complex problems and learn from raw sensory inputs. In this article, we will focus on AIRL as a DRL algorithm.
Adversarial Inverse Reinforcement Learning (AIRL): An Overview
AIRL is a model-free, deep inverse reinforcement learning algorithm that does not require rewards from the environment. Instead of relying on environmental feedback, AIRL uses a "discriminator" ANN to distinguish between expert observations and real observations generated by the same action. This innovative approach makes AIRL a promising RL algorithm for solving complex tasks.
In conclusion, reinforcement learning is a powerful machine learning approach that enables algorithms to learn from their environments. By understanding the different sub-classes of RL, such as IL and DRL, and exploring specific algorithms like AIRL, we can better comprehend the field of RL and its potential applications. With the help of everyday language and engaging analogies, we hope to demystify complex concepts and make RL more accessible to a wide audience.
