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 3151–3200 of 15113 papers

TitleStatusHype
Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading—0
A Reinforcement Learning-Based Task Mapping Method to Improve the Reliability of Clustered Manycores—0
Preventive Energy Management for Distribution Systems Under Uncertain Events: A Deep Reinforcement Learning Approach—0
Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RLCode0
Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization—0
Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search—0
Multimodal Deep Reinforcement Learning for Portfolio Optimization—0
Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities—0
Reinforcement Learning for Motor Control: A Comprehensive Review—0
ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning—0
Environment Descriptions for Usability and Generalisation in Reinforcement Learning—0
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps—0
Mathematics and Machine Creativity: A Survey on Bridging Mathematics with AI—0
On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds—0
Subgoal Discovery Using a Free Energy Paradigm and State Aggregations—0
Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed—0
Autonomous Option Invention for Continual Hierarchical Reinforcement Learning and PlanningCode0
VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving—0
From General to Specific: Tailoring Large Language Models for Personalized Healthcare—0
Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues—0
AdaCred: Adaptive Causal Decision Transformers with Feature Crediting—0
Learning to Generate Research Idea with Dynamic Control—0
Offline Safe Reinforcement Learning Using Trajectory ClassificationCode0
Single-Loop Federated Actor-Critic across Heterogeneous Environments—0
Deep reinforcement learning with time-scale invariant memoryCode0
Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control—0
Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models—0
Harvesting energy from turbulent winds with Reinforcement Learning—0
Tilted Quantile Gradient Updates for Quantile-Constrained Reinforcement LearningCode0
Multi-Task Reinforcement Learning for Quadrotors—0
Learning Visuotactile Estimation and Control for Non-prehensile Manipulation under Occlusions—0
CLIP-RLDrive: Human-Aligned Autonomous Driving via CLIP-Based Reward Shaping in Reinforcement Learning—0
ParMod: A Parallel and Modular Framework for Learning Non-Markovian Tasks—0
Design of Restricted Normalizing Flow towards Arbitrary Stochastic Policy with Computational Efficiency—0
Using machine learning to inform harvest control rule design in complex fishery settingsCode0
Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation—0
Equivariant Action Sampling for Reinforcement Learning and Planning—0
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents—0
MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization—0
MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning—0
Adaptive Reward Design for Reinforcement LearningCode0
Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement—0
Continuous-time optimal investment with portfolio constraints: a reinforcement learning approach—0
Physics Instrument Design with Reinforcement Learning—0
Reward Machine Inference for Robotic Manipulation—0
Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection—0
PickLLM: Context-Aware RL-Assisted Large Language Model Routing—0
Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning—0
From Text to Trajectory: Exploring Complex Constraint Representation and Decomposition in Safe Reinforcement Learning—0
Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer—0
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Benchmark Results

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