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

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Showing 2951–3000 of 15113 papers

TitleStatusHype
Comprehensive Review on the Control of Heat Pumps for Energy Flexibility in Distribution Networks—0
Optimizing Gene-Based Testing for Antibiotic Resistance Prediction—0
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage PlanningCode0
LocalEscaper: A Weakly-supervised Framework with Regional Reconstruction for Scalable Neural TSP Solvers—0
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex TasksCode0
Demystifying Multilingual Chain-of-Thought in Process Reward Modeling—0
A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models—0
RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning—0
EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning—0
Scaling Test-Time Compute Without Verification or RL is Suboptimal—0
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading—0
Robot Deformable Object Manipulation via NMPC-generated Demonstrations in Deep Reinforcement Learning—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
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces—0
VLP: Vision-Language Preference Learning for Embodied Manipulation—0
FitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control—0
CAMEL: Continuous Action Masking Enabled by Large Language Models for Reinforcement Learning—0
Addressing Moral Uncertainty using Large Language Models for Ethical Decision-Making—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
BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds—0
Dynamic Reinforcement Learning for Actors—0
Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations—0
Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation—0
Causal Information Prioritization for Efficient Reinforcement Learning—0
A Survey of Reinforcement Learning for Optimization in Automation—0
Diverse Transformer Decoding for Offline Reinforcement Learning Using Financial Algorithmic Approaches—0
Safe Reinforcement Learning-based Control for Hydrogen Diesel Dual-Fuel Engines—0
Necessary and Sufficient Oracles: Toward a Computational Taxonomy For Reinforcement Learning—0
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective—0
A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards—0
COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Grasping—0
Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters—0
Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning—0
Exploratory Diffusion Model for Unsupervised Reinforcement Learning—0
A Survey of In-Context 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
Active Advantage-Aligned Online Reinforcement Learning with Offline DataCode0
Advancing Autonomous VLM Agents via Variational Subgoal-Conditioned Reinforcement Learning—0
Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol—0
A view on learning robust goal-conditioned value functions: Interplay between RL and MPCCode0
Smell of Source: Learning-Based Odor Source Localization with Molecular Communication—0
Select before Act: Spatially Decoupled Action Repetition for Continuous Control—0
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
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Benchmark Results

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