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 1101–1150 of 15113 papers

TitleStatusHype
Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning—0
Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement LearningCode0
Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning—0
RaSS: Improving Denoising Diffusion Samplers with Reinforced Active Sampling Scheduler—0
A Graphical Approach to State Variable Selection in Off-policy Learning—0
Hybridising Reinforcement Learning and Heuristics for Hierarchical Directed Arc Routing ProblemsCode0
FORM: Learning Expressive and Transferable First-Order Logic Reward Machines—0
Towards Unraveling and Improving Generalization in World Models—0
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework—0
Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret—0
Weber-Fechner Law in Temporal Difference learning derived from Control as Inference—0
UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI—0
Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques—0
Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey—0
Exploiting Hybrid Policy in Reinforcement Learning for Interpretable Temporal Logic ManipulationCode1
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning—0
Efficient and Scalable Deep Reinforcement Learning for Mean Field Control GamesCode0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization—0
Preventive Energy Management for Distribution Systems Under Uncertain Events: A Deep Reinforcement Learning Approach—0
Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading—0
Provably Efficient Exploration in Reward Machines with Low Regret—0
xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of ExplainabilityCode0
Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning—0
A Reinforcement Learning-Based Task Mapping Method to Improve the Reliability of Clustered Manycores—0
Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RLCode0
HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMsCode5
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
Reinforcement Learning for Motor Control: A Comprehensive Review—0
Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities—0
Multimodal Deep Reinforcement Learning for Portfolio Optimization—0
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps—0
ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning—0
Environment Descriptions for Usability and Generalisation in Reinforcement Learning—0
Subgoal Discovery Using a Free Energy Paradigm and State Aggregations—0
Mathematics and Machine Creativity: A Survey on Bridging Mathematics with AI—0
On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds—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
From General to Specific: Tailoring Large Language Models for Personalized Healthcare—0
VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving—0
Offline Reinforcement Learning for LLM Multi-Step ReasoningCode2
AdaCred: Adaptive Causal Decision Transformers with Feature Crediting—0
Single-Loop Federated Actor-Critic across Heterogeneous Environments—0
Offline Safe Reinforcement Learning Using Trajectory ClassificationCode0
Deep reinforcement learning with time-scale invariant memoryCode0
Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues—0
Learning to Generate Research Idea with Dynamic Control—0
Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control—0
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Benchmark Results

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