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 676–700 of 15113 papers

TitleStatusHype
Handling Delay in Real-Time Reinforcement LearningCode0
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning—0
Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL—0
RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations—0
FLAM: Foundation Model-Based Body Stabilization for Humanoid Locomotion and Manipulation—0
Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments—0
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks—0
Pretrained Bayesian Non-parametric Knowledge Prior in Robotic Long-Horizon Reinforcement LearningCode0
Reward Design for Reinforcement Learning AgentsCode0
Controlling Large Language Model with Latent ActionsCode0
ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented GenerationCode1
UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement LearningCode2
Video-R1: Reinforcing Video Reasoning in MLLMsCode4
Reasoning Beyond Limits: Advances and Open Problems for LLMs—0
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion—0
Synthesizing world models for bilevel planning—0
The Crucial Role of Problem Formulation in Real-World Reinforcement Learning—0
Harmonia: A Multi-Agent Reinforcement Learning Approach to Data Placement and Migration in Hybrid Storage Systems—0
Understanding R1-Zero-Like Training: A Critical PerspectiveCode5
Learning Adaptive Dexterous Grasping from Single Demonstrations—0
Unlocking Efficient Long-to-Short LLM Reasoning with Model MergingCode2
Offline Reinforcement Learning with Discrete Diffusion Skills—0
Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal ControlCode0
Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation—0
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Benchmark Results

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