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

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Showing 7476–7500 of 15113 papers

TitleStatusHype
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning—0
Hyperparameter Tuning for Deep Reinforcement Learning Applications—0
Learning Invariable Semantical Representation from Language for Extensible Policy Generalization—0
Using Deep Reinforcement Learning for Zero Defect Smart Forging—0
MOORe: Model-based Offline-to-Online Reinforcement Learning—0
Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching—0
Accelerated Intravascular Ultrasound Imaging using Deep Reinforcement Learning—0
Generative Planning for Temporally Coordinated Exploration in Reinforcement LearningCode0
Large-Scale Graph Reinforcement Learning in Wireless Control Systems—0
State-Conditioned Adversarial Subgoal Generation—0
Understanding the Effects of Second-Order Approximations in Natural Policy Gradient Reinforcement LearningCode0
Online Attentive Kernel-Based Temporal Difference Learning—0
Multi-Agent Adversarial Attacks for Multi-Channel Communications—0
Reinforcement Learning Your Way: Agent Characterization through Policy Regularization—0
Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement LearningCode0
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search—0
Reinforcement Learning for Personalized Drug Discovery and Design for Complex Diseases: A Systems Pharmacology Perspective—0
Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning—0
Deep Reinforcement Learning with Spiking Q-learning—0
Instance-Dependent Confidence and Early Stopping for Reinforcement Learning—0
Environment Generation for Zero-Shot Compositional Reinforcement Learning—0
Deep reinforcement learning under signal temporal logic constraints using Lagrangian relaxation—0
A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning—0
Self-Awareness Safety of Deep Reinforcement Learning in Road Traffic Junction Driving—0
Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning—0
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Benchmark Results

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