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 2326–2350 of 15113 papers

TitleStatusHype
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