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 5426–5450 of 15113 papers

TitleStatusHype
RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning—0
RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN—0
RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs—0
RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems—0
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression—0
RLS3: RL-Based Synthetic Sample Selection to Enhance Spatial Reasoning in Vision-Language Models for Indoor Autonomous Perception—0
RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment—0
RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation—0
RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads—0
RL with KL penalties is better viewed as Bayesian inference—0
RLZero: Direct Policy Inference from Language Without In-Domain Supervision—0
Efficient Reinforcement Learning Development with RLzoo—0
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
ROAD: Responsibility-Oriented Reward Design for Reinforcement Learning in Autonomous Driving—0
Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach—0
ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR—0
Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning—0
Robo-advising: Learning Investors' Risk Preferences via Portfolio Choices—0
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment—0
Robot Deformable Object Manipulation via NMPC-generated Demonstrations in Deep Reinforcement Learning—0
Robot gains Social Intelligence through Multimodal Deep Reinforcement Learning—0
Robotic Arm Control and Task Training through Deep Reinforcement Learning—0
Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning—0
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Benchmark Results

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