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 5351–5400 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Multi-user Massive MIMO with Channel Aging—0
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEsCode0
To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs—0
Quantum algorithms applied to satellite mission planning for Earth observation—0
Regret-Based Defense in Adversarial Reinforcement LearningCode0
On Modeling Long-Term User Engagement from Stochastic Feedback—0
Universal Agent Mixtures and the Geometry of Intelligence—0
A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning—0
Maneuver Decision-Making For Autonomous Air Combat Through Curriculum Learning And Reinforcement Learning With Sparse Rewards—0
Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with DistractionsCode0
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning—0
Cross-domain Random Pre-training with Prototypes for Reinforcement LearningCode0
Low Entropy Communication in Multi-Agent Reinforcement Learning—0
A Survey on Causal Reinforcement Learning—0
Combining Reconstruction and Contrastive Methods for Multimodal Representations in RLCode0
Towards Minimax Optimality of Model-based Robust Reinforcement Learning—0
Scaling Goal-based Exploration via Pruning Proto-goals—0
Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning—0
Learning Complex Teamwork Tasks Using a Given Sub-task DecompositionCode0
CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning—0
Equivariant MuZero—0
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning—0
A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis—0
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning—0
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints—0
AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework—0
Near-Optimal Adversarial Reinforcement Learning with Switching Costs—0
Non-zero-sum Game Control for Multi-vehicle Driving via Reinforcement LearningCode0
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR—0
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective—0
Transfer learning for process design with reinforcement learning—0
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning—0
Online Reinforcement Learning with Uncertain Episode Lengths—0
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Adaptive Aggregation for Safety-Critical Control—0
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback—0
Arena-Web -- A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches—0
Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems—0
DITTO: Offline Imitation Learning with World Models—0
Intrinsic Rewards from Self-Organizing Feature Maps for Exploration in Reinforcement LearningCode0
A Strong Baseline for Batch Imitation Learning—0
State-wise Safe Reinforcement Learning: A Survey—0
RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads—0
Offline Learning in Markov Games with General Function Approximation—0
Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease TreatmentCode0
Open Problems and Modern Solutions for Deep Reinforcement Learning—0
Model-free Quantum Gate Design and Calibration using Deep Reinforcement LearningCode0
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage—0
An Online Model-Following Projection Mechanism Using Reinforcement Learning—0
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Benchmark Results

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