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 9376–9400 of 15113 papers

TitleStatusHype
RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search—0
Radiology Report Generation via Multi-objective Preference Optimization—0
RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning—0
RAIDER: Reinforcement-aided Spear Phishing Detector—0
Raijū: Reinforcement Learning-Guided Post-Exploitation for Automating Security Assessment of Network Systems—0
RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning—0
Railway Operation Rescheduling System via Dynamic Simulation and Reinforcement Learning—0
Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits—0
Random Copolymer inverse design system orienting on Accurate discovering of Antimicrobial peptide-mimetic copolymers—0
Random Ensemble Reinforcement Learning for Traffic Signal Control—0
Randomized Policy Learning for Continuous State and Action MDPs—0
Random Latent Exploration for Deep Reinforcement Learning—0
Random Network Distillation as a Diversity Metric for Both Image and Text Generation—0
RangL: A Reinforcement Learning Competition Platform—0
Ranking Items in Large-Scale Item Search Engines with Reinforcement Learning—0
Ranking sentences from product description & bullets for better search—0
Rapid Learning of Spatial Representations for Goal-Directed Navigation Based on a Novel Model of Hippocampal Place Fields—0
Rapid Locomotion via Reinforcement Learning—0
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data—0
RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning—0
RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation—0
RAP: Runtime-Adaptive Pruning for LLM Inference—0
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk—0
RaSS: Improving Denoising Diffusion Samplers with Reinforced Active Sampling Scheduler—0
Rate-matching the regret lower-bound in the linear quadratic regulator with unknown dynamics—0
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Benchmark Results

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