Top Research Papers on Reinforcement Learning

This work proposes Recovery RL, an algorithm which navigates this tradeoff by leveraging offline data to learn about constraint violating zones before policy learning and separating the goals of improving task performance and constraint satisfaction across two policies: a task policy that only optimizes the task reward and a recovery policy that guides the agent to safety when constraint violation is likely.

CFR-RL (Critical Flow Rerouting-Reinforcement Learning), a Reinforcement Learning-based scheme that learns a policy to select critical flows for each given traffic matrix automatically and reroutes these selected critical flows to balance link utilization of the network by formulating and solving a simple Linear Programming problem.

The RL-CycleGAN, a new approach for simulation-to-real-world transfer for reinforcement learning, is obtained by incorporating the RL-scene consistency loss into unsupervised domain translation, which ensures that the translation operation is invariant with respect to the Q-values associated with the image.

This work develops a reinforcement learning routing algorithm (RL-Routing) to solve a traffic engineering problem of SDN in terms of throughput and delay and considers comprehensive network information for state representation and use one-to-many network configuration for routing choices.

This paper presents GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies, and demonstrates that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer.

Through the experimental verification of multiple instances, it can be seen that the RL-GA can solve the EDSSP problem effectively and performs better in several aspects than the state-of-the-art algorithms.

sponsorship: This work emerged from the IBPSA Project 1, an international project conducted under the umbrella of the International Building Performance Simulation Association (IBPSA) . Project 1 will develop and demonstrate a BIM/GIS and Modelica Framework for building and community energy system design and operation. The work of Javier Arroyo is financed by VITO, Belgium through a PhD Fellowship (grant number 1710754) . Finally, the authors wish to thank to Brida V. Mbuwir, Jan Drgona, and Iago Cupeiro Figueroa for kindly reviewing the paper. (Project 1 will develop and demonstrate a BIM/GIS...

This is the first survey to focus on AD policy learning using DRL/DIL, which is addressed simultaneously from the system, task-driven and problem-driven perspectives.

A survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL is presented according to a novel taxonomy of the approaches.

This is the first comprehensive and self-contained introduction to deep reinforcement learning, covering all aspects from fundamentals and research to applications. It includes examples and codes to help readers practice and implement the techniques.

Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning has arisen to tackle various challenges faced by reinforcement learning, by transferring knowledge from external expertise to facilitate the efficiency and effectiveness of the learning process. In this survey, we systematically investigate the r...

Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same time minimizing the deviation from the behavior policy so as to avoid errors due to distributional shift. This trade-off is critical, because most current offline reinforcement learning methods need to query the value of unseen actions during training to improve the policy, and therefore need to either constrain these actions to be in-distribution, or else regularize their values. We propose an offline RL method that ne...

Conservative Q-learning (CQL) is proposed, which aims to address limitations of offline RL methods by learning a conservative Q-function such that the expected value of a policy under this Q- function lower-bounds its true value.

This review of talks and discussions in the "Deep Learning and Reinforcement Learning" session of the symposium, International Symposium on Artificial Intelligence and Brain Science, discusses whether the authors can achieve comprehensive understanding of human intelligence based on the recent advances of deep learning and reinforcement learning algorithms.

This survey systematically investigates the recent progress of transfer learning approaches in the context of deep reinforcement learning, and provides a framework for categorizing the state-of-the-art transfer Learning approaches under which to analyze their goals, methodologies, compatible reinforcement learning backbones, and practical applications.

This survey provides an extensive overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis, and many other control or scheduling problems that have infiltrated every aspect of the healthcare system.

A state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots.

It is argued that the field is well positioned to move beyond simplistic dichotomies, and a means of refocusing research questions towards the rich and complex components that comprise learning and decision-making is proposed.

The fundamental theories, key algorithms, and primary research domains of DRL, in addition to value-based and policy-based DRL algorithms, are summarized and the advances in maximum entropy- based DRL are summarized.

Learning from visual observations is a fundamental yet challenging problem in Reinforcement Learning (RL). Although algorithmic advances combined with convolutional neural networks have proved to be a recipe for success, current methods are still lacking on two fronts: (a) data-efficiency of learning and (b) generalization to new environments. To this end, we present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms. We perform the first extensive study of general data augmentations for RL on both pixel-based and state-based inp...