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 1001–1025 of 15113 papers

TitleStatusHype
Model-Free RL Agents Demonstrate System 1-Like Intentionality—0
Neural Operator based Reinforcement Learning for Control of first-order PDEs with Spatially-Varying State DelayCode0
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning—0
Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Information—0
RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems—0
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic LearningCode1
From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning—0
A Dual-Agent Adversarial Framework for Robust Generalization in Deep Reinforcement Learning—0
RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled PlatformsCode0
Integrating Reinforcement Learning and AI Agents for Adaptive Robotic Interaction and Assistance in Dementia Care—0
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
Exploratory Mean-Variance Portfolio Optimization with Regime-Switching Market Dynamics—0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning—0
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training—0
xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM JailbreakingCode1
Safe Reinforcement Learning for Real-World Engine Control—0
MPC4RL -- A Software Package for Reinforcement Learning based on Model Predictive Control—0
Flexible Blood Glucose Control: Offline Reinforcement Learning from Human Feedback—0
Towards General-Purpose Model-Free Reinforcement Learning—0
Selective Experience Sharing in Reinforcement Learning Enhances Interference Management—0
Benchmarking Quantum Reinforcement LearningCode0
FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities—0
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
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Benchmark Results

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