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 6601–6650 of 15113 papers

TitleStatusHype
Teaching a Robot to Walk Using Reinforcement Learning—0
PantheonRL: A MARL Library for Dynamic Training InteractionsCode1
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative FinanceCode2
Contextual Exploration Using a Linear Approximation Method Based on Satisficing—0
A Benchmark for Low-Switching-Cost Reinforcement Learning—0
Tree-based Focused Web Crawling with Reinforcement LearningCode1
OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical LocomotionCode1
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare—0
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement LearningCode2
Federated Reinforcement Learning at the Edge—0
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control—0
Formalising the Foundations of Discrete Reinforcement Learning in Isabelle/HOL—0
Faster Deep Reinforcement Learning with Slower Online NetworkCode1
Edge-Compatible Reinforcement Learning for Recommendations—0
Encoding priors in the brain: a reinforcement learning model for mouse decision making—0
Quantum Architecture Search via Continual Reinforcement Learning—0
Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots—0
Blockwise Sequential Model Learning for Partially Observable Reinforcement LearningCode0
How Private Is Your RL Policy? An Inverse RL Based Analysis FrameworkCode0
A Validation Tool for Designing Reinforcement Learning Environments—0
Cooperative Multi-Agent Reinforcement Learning with Hypergraph ConvolutionCode0
VMAgent: Scheduling Simulator for Reinforcement LearningCode1
Reinforcement Learning with Almost Sure Constraints—0
High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning—0
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
Learning multiple gaits of quadruped robot using hierarchical reinforcement learningCode1
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization—0
Learning to Select the Next Reasonable Mention for Entity Linking—0
Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market—0
Learning over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System—0
Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach—0
Suboptimal and trait-like reinforcement learning strategies correlate with midbrain encoding of prediction errors—0
ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical PerspectivesCode1
Recent Advances in Reinforcement Learning in Finance—0
Specializing Versatile Skill Libraries using Local Mixture of ExpertsCode0
CoMPS: Continual Meta Policy Search—0
Hyper-parameter optimization based on soft actor critic and hierarchical mixture regularization—0
A Review for Deep Reinforcement Learning in Atari:Benchmarks, Challenges, and Solutions—0
Application of Deep Reinforcement Learning to Payment Fraud—0
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning—0
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules—0
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless NetworksCode1
Godot Reinforcement Learning AgentsCode2
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach—0
Attention-Based Model and Deep Reinforcement Learning for Distribution of Event Processing TasksCode0
Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project—0
Tell me why! Explanations support learning relational and causal structureCode1
Model-free Nearly Optimal Control of Constrained-Input Nonlinear Systems Based on Synchronous Reinforcement Learning—0
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance—0
QKSA: Quantum Knowledge Seeking Agent -- resource-optimized reinforcement learning using quantum process tomography—0
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Benchmark Results

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