SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6251–6300 of 15113 papers

TitleStatusHype
Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics—0
Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters—0
Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory—0
Unified State Representation Learning under Data AugmentationCode0
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning—0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Checklist Models for Improved Output Fluency in Piano Fingering Prediction—0
Pathfinding in Random Partially Observable Environments with Vision-Informed Deep Reinforcement Learning—0
Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning—0
Safe Reinforcement Learning with Contrastive Risk Prediction—0
Ask Before You Act: Generalising to Novel Environments by Asking QuestionsCode0
Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning—0
Task-Agnostic Learning to Accomplish New Tasks—0
An Analysis of Deep Reinforcement Learning Agents for Text-based Games—0
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk—0
Robust Policy Optimization in Continuous-time Mixed H_2/H_ Stochastic Control—0
Reward Delay Attacks on Deep Reinforcement LearningCode0
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL—0
Non-iterative generation of an optimal mesh for a blade passage using deep reinforcement learning—0
Hybrid Supervised and Reinforcement Learning for the Design and Optimization of Nanophotonic Structures—0
Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep NeuroevolutionCode0
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning—0
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement LearningCode0
FORLORN: A Framework for Comparing Offline Methods and Reinforcement Learning for Optimization of RAN ParametersCode0
DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments—0
A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform—0
Concept-modulated model-based offline reinforcement learning for rapid generalization—0
A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem—0
Energy Optimization of Wind Turbines via a Neural Control Policy Based on Reinforcement Learning Markov Chain Monte Carlo Algorithm—0
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems—0
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning—0
Project proposal: A modular reinforcement learning based automated theorem proverCode0
Annealing Optimization for Progressive Learning with Stochastic ApproximationCode0
Finite-Time Error Bounds for Greedy-GQ—0
Improving Assistive Robotics with Deep Reinforcement Learning—0
Reinforcement learning-based optimised control for tracking of nonlinear systems with adversarial attacks—0
Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL AgentsCode0
SlateFree: a Model-Free Decomposition for Reinforcement Learning with Slate Actions—0
Natural Policy Gradients In Reinforcement Learning Explained—0
Prediction Based Decision Making for Autonomous Highway Driving—0
Variational Inference for Model-Free and Model-Based Reinforcement Learning—0
Model-Free Deep Reinforcement Learning in Software-Defined Networks—0
Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning—0
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification—0
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction—0
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning—0
Dialogue Evaluation with Offline Reinforcement Learning—0
A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning—0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Deep reinforcement learning for quantum multiparameter estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified