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 901–950 of 15113 papers

TitleStatusHype
Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?Code1
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage PlanningCode0
Addressing Moral Uncertainty using Large Language Models for Ethical Decision-Making—0
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV—0
Hovering Flight of Soft-Actuated Insect-Scale Micro Aerial Vehicles using Deep Reinforcement Learning—0
Learning to Sample Effective and Diverse Prompts for Text-to-Image GenerationCode1
Scaling Test-Time Compute Without Verification or RL is Suboptimal—0
CAMEL: Continuous Action Masking Enabled by Large Language Models for Reinforcement Learning—0
FitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control—0
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces—0
Robot Deformable Object Manipulation via NMPC-generated Demonstrations in Deep Reinforcement Learning—0
VLP: Vision-Language Preference Learning for Embodied Manipulation—0
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading—0
Evaluating the Paperclip Maximizer: Are RL-Based Language Models More Likely to Pursue Instrumental Goals?Code0
Scalable Multi-Agent Offline Reinforcement Learning and the Role of Information—0
Rule-Bottleneck Reinforcement Learning: Joint Explanation and Decision Optimization for Resource Allocation with Language Agents—0
Tackling the Zero-Shot Reinforcement Learning Loss Directly—0
Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation—0
Dynamic Reinforcement Learning for Actors—0
Causal Information Prioritization for Efficient Reinforcement Learning—0
Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement LearningCode2
BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds—0
Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations—0
Digi-Q: Learning Q-Value Functions for Training Device-Control AgentsCode2
Diverse Transformer Decoding for Offline Reinforcement Learning Using Financial Algorithmic Approaches—0
Safe Reinforcement Learning-based Control for Hydrogen Diesel Dual-Fuel Engines—0
A Survey of Reinforcement Learning for Optimization in Automation—0
Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing ProblemsCode1
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective—0
COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Grasping—0
Necessary and Sufficient Oracles: Toward a Computational Taxonomy For Reinforcement Learning—0
A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards—0
Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters—0
Advancing Autonomous VLM Agents via Variational Subgoal-Conditioned Reinforcement Learning—0
A Survey of In-Context Reinforcement Learning—0
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol—0
Active Advantage-Aligned Online Reinforcement Learning with Offline DataCode0
Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning—0
Exploratory Diffusion Model for Unsupervised Reinforcement Learning—0
Towards a Formal Theory of the Need for Competence via Computational Intrinsic Motivation—0
Near-Optimal Sample Complexity in Reward-Free Kernel-Based Reinforcement Learning—0
Exploring the Limit of Outcome Reward for Learning Mathematical ReasoningCode2
Smell of Source: Learning-Based Odor Source Localization with Molecular Communication—0
Select before Act: Spatially Decoupled Action Repetition for Continuous Control—0
A view on learning robust goal-conditioned value functions: Interplay between RL and MPCCode0
Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures—0
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language ModelsCode0
Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning—0
Learning Strategic Language Agents in the Werewolf Game with Iterative Latent Space Policy Optimization—0
Enhancing Pre-Trained Decision Transformers with Prompt-Tuning Bandits—0
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Benchmark Results

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