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 2076–2100 of 15113 papers

TitleStatusHype
CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality ResearchCode1
A Multimodal Learning-based Approach for Autonomous Landing of UAV—0
Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers—0
Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls—0
Feasibility Consistent Representation Learning for Safe Reinforcement LearningCode1
Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement LearningCode0
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning—0
Highway Graph to Accelerate Reinforcement LearningCode0
Comparisons Are All You Need for Optimizing Smooth Functions—0
Large Language Models are Biased Reinforcement LearnersCode0
Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning—0
Optimal control barrier functions for RL based safe powertrain control—0
Combined film and pulse heating of lithium ion batteries to improve performance in low ambient temperature—0
Towards Robust Policy: Enhancing Offline Reinforcement Learning with Adversarial Attacks and Defenses—0
LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions—0
Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail PromotionsCode0
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning—0
Stochastic Q-learning for Large Discrete Action Spaces—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning—0
CarDreamer: Open-Source Learning Platform for World Model based Autonomous DrivingCode3
vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvement—0
Reinformer: Max-Return Sequence Modeling for Offline RLCode1
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments—0
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Benchmark Results

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