State-of-the-Art Reinforcement Learning Algorithms
Deepanshu Mehta · International Journal of Engineering Research and · 2020
This research paper brings together many different aspects of the current research on several fields associated to Reinforcement Learning which has been growing rapidly, providing a wide variety of learning algorithms like Markov Decision Processes (MDPs), Temporal Difference (TD) Learning, Advantage Actor-Critic (A2C), Asynchronous Advantage Actor-Critic (A3C), Deep Q Networks (DQNs), Deep Deterministic Policy Gradient (DDPG) and Evolution Strategies (ES) for different applications.In this paper, the computations and procedures involved in Reinforcement Learning algorithms are briefly discussed.Reinforcement Learning can be used is almost every field for its automation and advancement.Nowadays, Meta-Learning, Automated Machine Learning and Self-Learning Systems have become very popular.Metalearning which is an application of evolution strategies is an exciting area of research that tackles the problem of learning to learn faster with being generalizable to many tasks.Automated machine learning is the process of automating end-to-end the process of applying machine learning to real-world problems.