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 4451–4500 of 15113 papers

TitleStatusHype
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning—0
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems—0
A gray-box approach for curriculum learning—0
A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model—0
A Guider Network for Multi-Dual Learning—0
A Guiding Principle for Causal Decision Problems—0
A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement—0
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines—0
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat—0
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning—0
A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction—0
A Hierarchical Model for Device Placement—0
A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks—0
A Hierarchical Two-tier Approach to Hyper-parameter Optimization in Reinforcement Learning—0
A Homogenization Approach for Gradient-Dominated Stochastic Optimization—0
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming—0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors—0
A Human Mixed Strategy Approach to Deep Reinforcement Learning—0
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression—0
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum—0
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming—0
A Hybrid PAC Reinforcement Learning Algorithm—0
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities—0
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving—0
AI Assisted Annotator using Reinforcement Learning—0
AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks—0
AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments—0
AI-based Robust Resource Allocation in End-to-End Network Slicing under Demand and CSI Uncertainties—0
AI-based traffic analysis in digital twin networks—0
AI-driven materials design: a mini-review—0
AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms—0
AI-Driven Resource Allocation in Optical Wireless Communication Systems—0
AIGB: Generative Auto-bidding via Conditional Diffusion Modeling—0
AIGenC: An AI generalisation model via creativity—0
AI Planning: A Primer and Survey (Preliminary Report)—0
AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning—0
AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning—0
AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference—0
AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework—0
AITuning: Machine Learning-based Tuning Tool for Run-Time Communication Libraries—0
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels—0
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces—0
A Language Model based Evaluator for Sentence Compression—0
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning—0
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning—0
A Learned Simulation Environment to Model Plant Growth in Indoor Farming—0
A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses—0
A Learning based Branch and Bound for Maximum Common Subgraph Problems—0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
A Learning Framework for High Precision Industrial Assembly—0
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Benchmark Results

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