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 2351–2375 of 15113 papers

TitleStatusHype
ReVeal: Self-Evolving Code Agents via Iterative Generation-Verification—0
Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy RegularizationCode0
Shapley Machine: A Game-Theoretic Framework for N-Agent Ad Hoc TeamworkCode0
PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier—0
Magistral—0
Automatic Treatment Planning using Reinforcement Learning for High-dose-rate Prostate Brachytherapy—0
A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications—0
Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flows—0
Bridging Continuous-time LQR and Reinforcement Learning via Gradient Flow of the Bellman Error—0
Policy-Based Trajectory Clustering in Offline Reinforcement Learning—0
DeepForm: Reasoning Large Language Model for Communication System Formulation—0
TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy OptimizationCode0
Exploration by Random Reward Perturbation—0
Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)—0
MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning—0
How to Provably Improve Return Conditioned Supervised Learning?—0
Offline RL with Smooth OOD Generalization in Convex Hull and its NeighborhoodCode0
Reinforcement Learning Teachers of Test Time Scaling—0
Optimal Operating Strategy for PV-BESS Households: Balancing Self-Consumption and Self-Sufficiency—0
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information—0
DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO—0
Through the Valley: Path to Effective Long CoT Training for Small Language Models—0
Reinforcement Pre-Training—0
AbstRaL: Augmenting LLMs' Reasoning by Reinforcing Abstract Thinking—0
LUCIFER: Language Understanding and Context-Infused Framework for Exploration and Behavior Refinement—0
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Benchmark Results

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