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 5851–5875 of 15113 papers

TitleStatusHype
Self-critical Sequence Training for Automatic Speech Recognition—0
Self-Driving Car Racing: Application of Deep Reinforcement Learning—0
Self-driving scale car trained by Deep reinforcement learning—0
Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning—0
Self-evolving Autoencoder Embedded Q-Network—0
Self-Evolving Curriculum for LLM Reasoning—0
Self-Imitation Advantage Learning—0
Self-Imitation Learning by Planning—0
Self-Imitation Learning from Demonstrations—0
Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning—0
Self-Inspection Method of Unmanned Aerial Vehicles in Power Plants Using Deep Q-Network Reinforcement Learning—0
Self-Learned Formula Synthesis in Set Theory—0
Self-Learning Tuning for Post-Silicon Validation—0
Self-optimizing adaptive optics control with Reinforcement Learning for high-contrast imaging—0
Self-organization in a distributed coordination game through heuristic rules—0
Self-Organizing Maps as a Storage and Transfer Mechanism in Reinforcement Learning—0
Self-Organizing Maps for Storage and Transfer of Knowledge in Reinforcement Learning—0
Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation—0
Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games—0
Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Models—0
Self-Supervised Continuous Control without Policy Gradient—0
Self-Supervised Exploration via Temporal Inconsistency in Reinforcement Learning—0
Self-supervised Learning of Distance Functions for Goal-Conditioned Reinforcement Learning—0
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning—0
Self-Supervised Reinforcement Learning for Recommender Systems—0
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Benchmark Results

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