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 2951–2975 of 15113 papers

TitleStatusHype
Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation—0
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and SmoothnessCode0
Robust Offline Reinforcement Learning -- Certify the Confidence Interval—0
RLLTE: Long-Term Evolution Project of Reinforcement LearningCode2
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments—0
Stackelberg Batch Policy Learning—0
Efficiency Separation between RL Methods: Model-Free, Model-Based and Goal-Conditioned—0
Raijū: Reinforcement Learning-Guided Post-Exploitation for Automating Security Assessment of Network Systems—0
Tempo Adaptation in Non-stationary Reinforcement LearningCode0
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement LearningCode2
Zero-Shot Reinforcement Learning from Low Quality DataCode1
PlotMap: Automated Layout Design for Building Game WorldsCode0
Recurrent Hypernetworks are Surprisingly Strong in Meta-RLCode1
A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies—0
ODE-based Recurrent Model-free Reinforcement Learning for POMDPs—0
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds—0
On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors—0
Enhancing data efficiency in reinforcement learning: a novel imagination mechanism based on mesh information propagationCode1
Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning—0
Iterative Reachability Estimation for Safe Reinforcement Learning—0
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based RolloutCode0
Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills—0
Reinforcement Learning for Robust Header Compression under Model Uncertainty—0
Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs—0
Offline to Online Learning for Real-Time Bandwidth Estimation—0
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Benchmark Results

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