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 3676–3700 of 15113 papers

TitleStatusHype
ProSpec RL: Plan Ahead, then Execute—0
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations—0
A Method for Fast Autonomy Transfer in Reinforcement Learning—0
Appraisal-Guided Proximal Policy Optimization: Modeling Psychological Disorders in Dynamic Grid World—0
Evolution of cooperation in the public goods game with Q-learning—0
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning—0
Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems—0
QT-TDM: Planning With Transformer Dynamics Model and Autoregressive Q-Learning—0
Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers—0
Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning—0
SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning—0
Path Following and Stabilisation of a Bicycle Model using a Reinforcement Learning Approach—0
Pretrained Visual Representations in Reinforcement Learning—0
Sublinear Regret for a Class of Continuous-Time Linear-Quadratic Reinforcement Learning Problems—0
From Imitation to Refinement -- Residual RL for Precise Assembly—0
Automatic Environment Shaping is the Next Frontier in RL—0
ODGR: Online Dynamic Goal Recognition—0
SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees—0
Functional Acceleration for Policy Mirror DescentCode0
Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments—0
Artificial Intelligence-based Decision Support Systems for Precision and Digital Health—0
Should we use model-free or model-based control? A case study of battery management systems—0
Concept-Based Interpretable Reinforcement Learning with Limited to No Human Labels—0
Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications—0
Offline Imitation Learning Through Graph Search and Retrieval—0
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Benchmark Results

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