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 2301–2350 of 15113 papers

TitleStatusHype
Curriculum-Guided Antifragile Reinforcement Learning for Secure UAV Deconfliction under Observation-Space Attacks—0
Homogenization of Multi-agent Learning Dynamics in Finite-state Markov GamesCode0
Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards—0
Complex Model Transformations by Reinforcement Learning with Uncertain Human GuidanceCode0
Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative ControlCode0
Causal-Aware Intelligent QoE Optimization for VR Interaction with Adaptive Keyframe Extraction—0
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis—0
Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion—0
Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution GridsCode0
Robots and Children that Learn Together : Improving Knowledge Retention by Teaching Peer-Like Interactive Robots—0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model—0
Accelerating Residual Reinforcement Learning with Uncertainty Estimation—0
Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking—0
Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy EvaluationCode0
Learning Dexterous Object Handover—0
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using SparsityCode0
VRAIL: Vectorized Reward-based Attribution for Interpretable Learning—0
Dual-Objective Reinforcement Learning with Novel Hamilton-Jacobi-Bellman Formulations—0
Multi-Task Lifelong Reinforcement Learning for Wireless Sensor Networks—0
From General to Targeted Rewards: Surpassing GPT-4 in Open-Ended Long-Context Generation—0
Multi-Agent Reinforcement Learning for Autonomous Multi-Satellite Earth Observation: A Realistic Case Study—0
Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks—0
Steering Your Diffusion Policy with Latent Space Reinforcement Learning—0
Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access—0
PeRL: Permutation-Enhanced Reinforcement Learning for Interleaved Vision-Language Reasoning—0
Adaptive Reinforcement Learning for Unobservable Random Delays—0
Reasoning with Exploration: An Entropy Perspective—0
Zeroth-Order Optimization is Secretly Single-Step Policy Optimization—0
Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs—0
IntelliLung: Advancing Safe Mechanical Ventilation using Offline RL with Hybrid Actions and Clinically Aligned Rewards—0
HiLight: A Hierarchical Reinforcement Learning Framework with Global Adversarial Guidance for Large-Scale Traffic Signal Control—0
Unsupervised Skill Discovery through Skill Regions Differentiation—0
Socratic RL: A Novel Framework for Efficient Knowledge Acquisition through Iterative Reflection and Viewpoint Distillation—0
ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture—0
Overcoming Overfitting in Reinforcement Learning via Gaussian Process Diffusion PolicyCode0
AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy—0
A Technical Study into Small Reasoning Language Models—0
StaQ it! Growing neural networks for Policy Mirror Descent—0
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning—0
Value-Free Policy Optimization via Reward PartitioningCode0
Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes—0
Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning—0
RL-Guided MPC for Autonomous Greenhouse Control—0
Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xApps—0
CAPO: Reinforcing Consistent Reasoning in Medical Decision-Making—0
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under UncertaintyCode0
MM-R5: MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning for Document RetrievalCode0
ReVeal: Self-Evolving Code Agents via Iterative Generation-Verification—0
Automated Treatment Planning for Interstitial HDR Brachytherapy for Locally Advanced Cervical Cancer using Deep Reinforcement Learning—0
Eliciting Reasoning in Language Models with Cognitive Tools—0
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Benchmark Results

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