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 8476–8500 of 15113 papers

TitleStatusHype
Recommendation Fairness: From Static to Dynamic—0
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
Self-timed Reinforcement Learning using Tsetlin Machine—0
Reinforcement Learning for Battery Energy Storage Dispatch augmented with Model-based Optimizer—0
Multi-Agent Inverse Reinforcement Learning: Suboptimal Demonstrations and Alternative Solution Concepts—0
An Oracle and Observations for the OpenAI Gym / ALE Freeway Environment—0
Boosting Search Engines with Interactive Agents—0
Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge DistillationCode0
A Survey of Exploration Methods in Reinforcement Learning—0
Variational Quantum Reinforcement Learning via Evolutionary Optimization—0
OptAGAN: Entropy-based finetuning on text VAE-GANCode0
Incorporating Deception into CyberBattleSim for Autonomous Defense—0
Informing Autonomous Deception Systems with Cyber Expert Performance Data—0
Investigating Vulnerabilities of Deep Neural Policies—0
Adaptive perturbation adversarial training: based on reinforcement learning—0
Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning—0
Integrated Decision and Control at Multi-Lane Intersections with Mixed Traffic Flow—0
Identifying optimal cycles in quantum thermal machines with reinforcement-learningCode0
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning—0
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models—0
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents—0
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Benchmark Results

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