Top Research Papers on Reinforcement Learning

This vignette gives an introduction to the ReinforcementLearning package, which allows one to perform model-free reinforcement in R and presents methods to customize the learning and action selection behavior of the agent.

This paper demonstrates how to perform reinforcement learning in R and introduces the ReinforcementLearning package, which provides a remarkably flexible framework and is easily applied to a wide range of different problems.

The goal is to maximize the energy efficiency of the EH-WBANs with the joint consideration of transmission mode, relay selection, allocated time slot, transmission power, and the energy constraint of each sensor.

The oscillations and spikes in the early part of the curve for the optimistic method are explained, which makes this method perform differently on particular early plays.

The aim of the coursework is to better familiarise you with function approximation and sampling in the context of Reinforcement Learning (RL).

The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning, including surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations.

Reinforcement learning is an approach to artificial intelligence that emphasizes learning by the individual from its interaction with its environment. This contrasts with classical approaches to artificial intelligence and machine learning, which have downplayed learning from interaction, focusing instead on learning from a knowledgeable teacher, or on reasoning from a complete model of the environment. Modern reinforcement learning research is highly interdisciplinary; it includes researchers specializing in operations research, genetic algorithms, neural networks, psychology, and control eng...

The discussion here considers a much more common learning condition where an agent has to learn to make decisions in the environment from simple feedback, where feedback is provided only after periods of actions in the form of reward or punishment.

This paper will present a state of the art overview of reinforcement learning and how it can benefit astronomy.

This work proposes an adaptive and holistic method of routing and wavelength assignment (RWA) based on Reinforcement Learning (RL) based on Reinforcement Learning for ONoCs by using intelligent learning algorithms to process and learn the dynamic on-chip network information in multi-dimensional.

1 Chandrasekar Vuppalapati, 2 Anitha Ilapakurti, 3 Jayashankar Vuppaati, 4 Santosh Kedari 1 Hanumayamma Innovations and Technologies, Inc.

This work introduces a novel approach to deep meta-reinforcement learning, which is a system that is trained using one RL algorithm, but whose recurrent dynamics implement a second, quite separate RL procedure.

This thesis will extend the concept of transfer learning to curriculum learning, where the goal is to design a sequence of source tasks for an agent to train on, such that final performance or learning speed is improved.

It is shown that MA-RL framework can achieve the best-Figure of Merits for complex analog circuits’ design and shines the light for future large scale analog circuit system design automation.

This article uses temporal difference error minimization and stochastic approximation to develop a scalable RL algorithm to compute an ARNE of nonzero-sum stochastic games and proves the convergence of the algorithm to an ARNE.

This textbook covers principles behind main modern deep reinforcement learning algorithms that achieved breakthrough results in many domains from game AI to robotics.

This paper talks about the reinforcement learning in the perspective of Markov Decision Process and Partially Observable Markov decision process, which are the core algorithms in reinforcement learning.

In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.

This work aims to incorporate RL into E in a way that generalizes the approach taken by E’s --auto mode.

This essay compares some basic algorithms related to RL to help reader to have a basic understanding of RL and propose some exsiting defects about it.