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 3776–3800 of 15113 papers

TitleStatusHype
Exploiting Unlabeled Data for Feedback Efficient Human Preference based Reinforcement Learning—0
Swapped goal-conditioned offline reinforcement learningCode1
Dual RL: Unification and New Methods for Reinforcement and Imitation LearningCode1
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning—0
Tuning computer vision models with task rewards—0
Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control—0
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications—0
Optimal Sample Complexity of Reinforcement Learning for Mixing Discounted Markov Decision Processes—0
Scalable Multi-Agent Reinforcement Learning with General Utilities—0
Prioritized offline Goal-swapping Experience Replay—0
CERiL: Continuous Event-based Reinforcement Learning—0
Meta-Reinforcement Learning via Exploratory Task Clustering—0
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEsCode0
Deep Reinforcement Learning for Multi-user Massive MIMO with Channel Aging—0
Quantum algorithms applied to satellite mission planning for Earth observation—0
To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs—0
Constrained Decision Transformer for Offline Safe Reinforcement LearningCode2
Regret-Based Defense in Adversarial Reinforcement LearningCode0
Semiconductor Fab Scheduling with Self-Supervised and Reinforcement LearningCode1
On Modeling Long-Term User Engagement from Stochastic Feedback—0
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement LearningCode1
A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning—0
Guiding Pretraining in Reinforcement Learning with Large Language ModelsCode1
Universal Agent Mixtures and the Geometry of Intelligence—0
Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with DistractionsCode0
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Benchmark Results

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