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

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Showing 2451–2500 of 15113 papers

TitleStatusHype
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
OIL-AD: An Anomaly Detection Framework for Sequential Decision SequencesCode0
Learning Diverse Policies with Soft Self-Generated Guidance—0
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs—0
Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy—0
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model FeedbackCode2
SEABO: A Simple Search-Based Method for Offline Imitation LearningCode1
Averaging n-step Returns Reduces Variance in Reinforcement Learning—0
No-Regret Reinforcement Learning in Smooth MDPs—0
Reinforcement Learning from Bagged Reward—0
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning AgentsCode0
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement LearningCode1
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgentCode0
Abstracted Trajectory Visualization for Explainability in Reinforcement Learning—0
Frugal Actor-Critic: Sample Efficient Off-Policy Deep Reinforcement Learning Using Unique Experiences—0
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance—0
DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment DesignCode0
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation ProblemCode0
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement LearningCode3
Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning—0
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning—0
Understanding What Affects the Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence—0
Vision-Language Models Provide Promptable Representations for Reinforcement Learning—0
Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate—0
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short DelaysCode0
Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human ElbowsCode0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
The Virtues of Pessimism in Inverse Reinforcement Learning—0
Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach—0
A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control—0
Adaptive Q-Aid for Conditional Supervised Learning in Offline Reinforcement Learning—0
A Survey of Constraint Formulations in Safe Reinforcement Learning—0
Rethinking the Role of Proxy Rewards in Language Model AlignmentCode0
The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models—0
The Political Preferences of LLMs—0
An Auction-based Marketplace for Model Trading in Federated Learning—0
To the Max: Reinventing Reward in Reinforcement LearningCode0
StepCoder: Improve Code Generation with Reinforcement Learning from Compiler FeedbackCode2
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems—0
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation LearningCode0
ODICE: Revealing the Mystery of Distribution Correction Estimation via Orthogonal-gradient UpdateCode1
Towards Efficient Exact Optimization of Language Model AlignmentCode2
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk ManagementCode2
Leveraging Approximate Model-based Shielding for Probabilistic Safety Guarantees in Continuous EnvironmentsCode0
Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances—0
A Reinforcement Learning Based Controller to Minimize Forces on the Crutches of a Lower-Limb Exoskeleton—0
A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC GuaranteesCode0
Causal Coordinated Concurrent Reinforcement Learning—0
Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning—0
Zero-Shot Reinforcement Learning via Function EncodersCode0
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Benchmark Results

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