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 8751–8800 of 15113 papers

TitleStatusHype
R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning—0
R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery—0
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning—0
RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning—0
RACE: A Reinforcement Learning Framework for Improved Adaptive Control of NoC Channel Buffers—0
Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car—0
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
Rating Continuous Actions in Spatial Multi-Agent Problems—0
Innate-Values-driven Reinforcement Learning based Cognitive Modeling—0
Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)—0
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning—0
RBED: Reward Based Epsilon Decay—0
RbRL2.0: Integrated Reward and Policy Learning for Rating-based Reinforcement Learning—0
Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks—0
Reachability analysis in stochastic directed graphs by reinforcement learning—0
Reachability-Aware Laplacian Representation in Reinforcement Learning—0
Reachability Traces for Curriculum Design in Reinforcement Learning—0
Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning—0
Reactive Reinforcement Learning in Asynchronous Environments—0
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning—0
Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation—0
REALab: An Embedded Perspective on Tampering—0
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World—0
Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning—0
Offline to Online Learning for Real-Time Bandwidth Estimation—0
Real-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction—0
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Benchmark Results

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