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 6201–6225 of 15113 papers

TitleStatusHype
Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning—0
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory—0
Statistical Inference After Adaptive Sampling for Longitudinal Data—0
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning—0
Statistics and Samples in Distributional Reinforcement Learning—0
Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm—0
Steady State Analysis of Episodic Reinforcement Learning—0
Steady-State Error Compensation for Reinforcement Learning with Quadratic Rewards—0
Stealing Deep Reinforcement Learning Models for Fun and Profit—0
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning—0
Steering LLM Reasoning Through Bias-Only Adaptation—0
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning—0
Steering Your Diffusion Policy with Latent Space Reinforcement Learning—0
Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning—0
Stein Variational Policy Gradient—0
Stepping Out of the Shadows: Reinforcement Learning in Shadow Mode—0
Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs—0
Stigmergic Independent Reinforcement Learning for Multi-Agent Collaboration—0
Stochastically Dominant Distributional Reinforcement Learning—0
Stochastic Approximation of Gaussian Free Energy for Risk-Sensitive Reinforcement Learning—0
Stochastic Approximation with Markov Noise: Analysis and applications in reinforcement learning—0
Stochastic Constraint Programming as Reinforcement Learning—0
Stochastic convex optimization for provably efficient apprenticeship learning—0
Stochastic evolution in populations of ideas—0
Stochastic Gradient Descent with Dependent Data for Offline Reinforcement Learning—0
Show:102550
← PrevPage 249 of 605Next →

Benchmark Results

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