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 10576–10600 of 15113 papers

TitleStatusHype
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation—0
SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution—0
Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning—0
Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning—0
Switching Linear Dynamics for Variational Bayes Filtering—0
Switching the Loss Reduces the Cost in Batch Reinforcement Learning—0
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents—0
Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning—0
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning—0
Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models—0
Symbolic Regression Methods for Reinforcement Learning—0
Symbolic Reinforcement Learning for Safe RAN Control—0
Symmetry-aware Neural Architecture for Embodied Visual Navigation—0
Symmetry-Aware Neural Architecture for Embodied Visual Exploration—0
Symmetry Detection in Trajectory Data for More Meaningful Reinforcement Learning Representations—0
Symmetry Learning for Function Approximation in Reinforcement Learning—0
Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics—0
Synchronous vs Asynchronous Reinforcement Learning in a Real World Robot—0
Synergistic Formulaic Alpha Generation for Quantitative Trading based on Reinforcement Learning—0
Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security—0
Synthesizing Chemical Plant Operation Procedures using Knowledge, Dynamic Simulation and Deep Reinforcement Learning—0
Synthesizing Programmatic Policies that Inductively Generalize—0
Synthesizing Safe Policies under Probabilistic Constraints with Reinforcement Learning and Bayesian Model Checking—0
Synthesizing world models for bilevel planning—0
Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project—0
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Benchmark Results

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