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 5051–5100 of 15113 papers

TitleStatusHype
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory—0
Simple Noisy Environment Augmentation for Reinforcement LearningCode0
Rethinking Population-assisted Off-policy Reinforcement Learning—0
Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender SystemsCode0
Gym-preCICE: Reinforcement Learning Environments for Active Flow Control—0
An Autonomous Non-monolithic Agent with Multi-mode Exploration based on Options FrameworkCode0
Validation of massively-parallel adaptive testing using dynamic control matching—0
Sample Efficient Model-free Reinforcement Learning from LTL Specifications with Optimality GuaranteesCode0
A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings—0
Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learning—0
A Federated Reinforcement Learning Framework for Link Activation in Multi-link Wi-Fi Networks—0
One-Step Distributional Reinforcement Learning—0
Reinforcement Learning with Partial Parametric Model Knowledge—0
Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach—0
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation ProcessingCode0
Distance Weighted Supervised Learning for Offline Interaction DataCode0
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution TrajectoriesCode0
A Closer Look at Reward Decomposition for High-Level Robotic Explanations—0
Loss- and Reward-Weighting for Efficient Distributed Reinforcement Learning—0
Proximal Curriculum for Reinforcement Learning AgentsCode0
Model Extraction Attacks Against Reinforcement Learning Based Controllers—0
What can online reinforcement learning with function approximation benefit from general coverage conditions?—0
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes—0
Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey—0
Policy Resilience to Environment Poisoning Attacks on Reinforcement Learning—0
Reinforcement Learning Approaches for Traffic Signal Control under Missing DataCode0
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning—0
A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making—0
End-to-End Policy Gradient Method for POMDPs and Explainable Agents—0
Learning and Adapting Agile Locomotion Skills by Transferring Experience—0
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing—0
Cooperative Multi-Agent Reinforcement Learning for Inventory Management—0
Feasible Policy Iteration for Safe Reinforcement Learning—0
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning—0
An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems—0
Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated EnvironmentsCode0
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed—0
TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithmCode0
Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning—0
Exploring the Noise Resilience of Successor Features and Predecessor Features Algorithms in One and Two-Dimensional Environments—0
Towards Controllable Diffusion Models via Reward-Guided Exploration—0
Car-Following Models: A Multidisciplinary Review—0
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning—0
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning—0
Multi-agent Policy Reciprocity with Theoretical Guarantee—0
Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation—0
Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives—0
Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resamplingCode0
Control invariant set enhanced reinforcement learning for process control: improved sampling efficiency and guaranteed stability—0
Feudal Graph Reinforcement LearningCode0
Show:102550
← PrevPage 102 of 303Next →

Benchmark Results

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