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 2901–2950 of 15113 papers

TitleStatusHype
Distributional Reinforcement Learning with Online Risk-awareness Adaption—0
DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather DataCode0
Learning Generalizable Agents via Saliency-Guided Features Decorrelation—0
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning ModelsCode1
Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration—0
Reinforced UI Instruction Grounding: Towards a Generic UI Task Automation API—0
Optimal Sequential Decision-Making in Geosteering: A Reinforcement Learning Approach—0
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced DatasetsCode1
Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning—0
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison—0
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement LearningCode1
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems—0
Optimal Control of District Cooling Energy Plant with Reinforcement Learning and MPC—0
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels—0
LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option FrameworkCode0
Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End—0
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning—0
How the level sampling process impacts zero-shot generalisation in deep reinforcement learning—0
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms—0
Discovering General Reinforcement Learning Algorithms with Adversarial Environment DesignCode1
Proximal Policy Optimization-Based Reinforcement Learning Approach for DC-DC Boost Converter Control: A Comparative Evaluation Against Traditional Control Techniques—0
B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis—0
Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationCode0
Neural architecture impact on identifying temporally extended Reinforcement Learning tasks—0
Searching for High-Value Molecules Using Reinforcement Learning and Transformers—0
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own—0
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making—0
PGDQN: Preference-Guided Deep Q-NetworkCode1
Navigating Uncertainty in ESG Investing—0
On Representation Complexity of Model-based and Model-free Reinforcement Learning—0
Prioritized Soft Q-Decomposition for Lexicographic Reinforcement LearningCode0
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback—0
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model—0
Towards a Unified Framework for Sequential Decision Making—0
Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiencyCode0
Blending Imitation and Reinforcement Learning for Robust Policy Improvement—0
REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression—0
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning—0
Improving Dialogue Management: Quality Datasets vs ModelsCode0
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information—0
Adaptive Control of an Inverted Pendulum by a Reinforcement Learning-based LQR Method—0
Controlling Neural Style Transfer with Deep Reinforcement Learning—0
Improving Planning with Large Language Models: A Modular Agentic ArchitectureCode1
Learning to Rewrite Prompts for Personalized Text Generation—0
Reinforcement Learning for Node Selection in Branch-and-Bound—0
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and SmoothnessCode0
AdaRefiner: Refining Decisions of Language Models with Adaptive FeedbackCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill DiscoveryCode0
A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning—0
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Benchmark Results

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