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 7526–7550 of 15113 papers

TitleStatusHype
Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning—0
Recommendation Fairness: From Static to Dynamic—0
Temporal Shift Reinforcement LearningCode0
On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games—0
Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms—0
Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach—0
Multi-agent Natural Actor-critic Reinforcement Learning Algorithms—0
Unsupervised multi-latent space reinforcement learning framework for video summarization in ultrasound imagingCode0
Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer—0
Self-timed Reinforcement Learning using Tsetlin Machine—0
Multi-Agent Inverse Reinforcement Learning: Suboptimal Demonstrations and Alternative Solution Concepts—0
A Comparative Study of Algorithms for Intelligent Traffic Signal ControlCode2
An Oracle and Observations for the OpenAI Gym / ALE Freeway Environment—0
A Survey of Exploration Methods in Reinforcement Learning—0
Boosting Search Engines with Interactive Agents—0
Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge DistillationCode0
OptAGAN: Entropy-based finetuning on text VAE-GANCode0
Variational Quantum Reinforcement Learning via Evolutionary Optimization—0
Informing Autonomous Deception Systems with Cyber Expert Performance Data—0
Incorporating Deception into CyberBattleSim for Autonomous Defense—0
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPUCode1
Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive SummarizationCode1
Deep Reinforcement Learning at the Edge of the Statistical PrecipiceCode1
Investigating Vulnerabilities of Deep Neural Policies—0
Adaptive perturbation adversarial training: based on reinforcement learning—0
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Benchmark Results

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