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 2651–2675 of 15113 papers

TitleStatusHype
World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks—0
Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion—0
SmallPlan: Leverage Small Language Models for Sequential Path Planning with Simulation-Powered, LLM-Guided DistillationCode0
A General Approach of Automated Environment Design for Learning the Optimal Power Flow—0
Implicit Neural-Representation Learning for Elastic Deformable-Object Manipulations—0
Leveraging Partial SMILES Validation Scheme for Enhanced Drug Design in Reinforcement Learning Frameworks—0
Directly Forecasting Belief for Reinforcement Learning with DelaysCode0
MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion—0
Phi-4-reasoning Technical Report—0
Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models—0
Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning—0
Enhancing New-item Fairness in Dynamic Recommender SystemsCode0
Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math—0
A Survey on GUI Agents with Foundation Models Enhanced by Reinforcement Learning—0
PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations—0
Token-Efficient RL for LLM Reasoning—0
AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning—0
Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving—0
Reinforcement Learning-Based Heterogeneous Multi-Task Optimization in Semantic Broadcast Communications—0
An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination—0
LLMs for Engineering: Teaching Models to Design High Powered Rockets—0
BQSched: A Non-intrusive Scheduler for Batch Concurrent Queries via Reinforcement LearningCode0
Explainable AI for UAV Mobility Management: A Deep Q-Network Approach for Handover Minimization—0
LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN—0
Depth-Constrained ASV Navigation with Deep RL and Limited Sensing—0
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Benchmark Results

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