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

Showing 1055110600 of 15113 papers

TitleStatusHype
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)0
Supervised Pretraining Can Learn In-Context Reinforcement Learning0
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation0
SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning0
Supplementing Gradient-Based Reinforcement Learning with Simple Evolutionary Ideas0
SURF: Semantic-level Unsupervised Reward Function for Machine Translation0
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning0
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning0
SURREAL-System: Fully-Integrated Stack for Distributed Deep Reinforcement Learning0
Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network0
Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning0
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles0
Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training0
Survey on Fair Reinforcement Learning: Theory and Practice0
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods0
Survey on Multi-Agent Q-Learning frameworks for resource management in wireless sensor network0
Survey on reinforcement learning for language processing0
Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach0
Surveys without Questions: A Reinforcement Learning Approach0
Survival Analysis on Structured Data using Deep Reinforcement Learning0
Survival Instinct in Offline Reinforcement Learning0
Survival of the Fittest: Evolutionary Adaptation of Policies for Environmental Shifts0
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning0
SVQN: Sequential Variational Soft Q-Learning Networks0
Swarm Behavior Cloning0
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation0
SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution0
Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning0
Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning0
Switching Linear Dynamics for Variational Bayes Filtering0
Switching the Loss Reduces the Cost in Batch Reinforcement Learning0
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents0
Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning0
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning0
Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models0
Symbolic Regression Methods for Reinforcement Learning0
Symbolic Reinforcement Learning for Safe RAN Control0
Symmetry-aware Neural Architecture for Embodied Visual Navigation0
Symmetry-Aware Neural Architecture for Embodied Visual Exploration0
Symmetry Detection in Trajectory Data for More Meaningful Reinforcement Learning Representations0
Symmetry Learning for Function Approximation in Reinforcement Learning0
Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics0
Synchronous vs Asynchronous Reinforcement Learning in a Real World Robot0
Synergistic Formulaic Alpha Generation for Quantitative Trading based on Reinforcement Learning0
Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security0
Synthesizing Chemical Plant Operation Procedures using Knowledge, Dynamic Simulation and Deep Reinforcement Learning0
Synthesizing Programmatic Policies that Inductively Generalize0
Synthesizing Safe Policies under Probabilistic Constraints with Reinforcement Learning and Bayesian Model Checking0
Synthesizing world models for bilevel planning0
Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project0
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Benchmark Results

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