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 3051–3075 of 15113 papers

TitleStatusHype
Exploratory Mean-Variance Portfolio Optimization with Regime-Switching Market Dynamics—0
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training—0
RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled PlatformsCode0
Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies—0
Improving Vision-Language-Action Model with Online Reinforcement Learning—0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning—0
Selective Experience Sharing in Reinforcement Learning Enhances Interference Management—0
Towards General-Purpose Model-Free Reinforcement Learning—0
FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities—0
Flexible Blood Glucose Control: Offline Reinforcement Learning from Human Feedback—0
MPC4RL -- A Software Package for Reinforcement Learning based on Model Predictive Control—0
Benchmarking Quantum Reinforcement LearningCode0
Learning-Enhanced Safeguard Control for High-Relative-Degree Systems: Robust Optimization under Disturbances and Faults—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement LearningCode0
Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction—0
Data Center Cooling System Optimization Using Offline Reinforcement Learning—0
Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework—0
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks—0
Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control—0
Large Language Model driven Policy Exploration for Recommender Systems—0
State Combinatorial Generalization In Decision Making With Conditional Diffusion Models—0
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization—0
Adaptive Data Exploitation in Deep Reinforcement LearningCode0
On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration—0
Exploring the Technology Landscape through Topic Modeling, Expert Involvement, and Reinforcement Learning—0
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Benchmark Results

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