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 6151–6200 of 15113 papers

TitleStatusHype
Multi-fidelity reinforcement learning framework for shape optimization—0
Reward-Free Policy Space Compression for Reinforcement Learning—0
A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric VehiclesCode1
Continual Auxiliary Task Learning—0
Behaviour-neutral Smart Charging of Plugin Electric Vehicles: Reinforcement learning approach—0
A policy gradient approach for optimization of smooth risk measures—0
Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach—0
A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning—0
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning—0
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing—0
Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks—0
Autonomous Warehouse Robot using Deep Q-Learning—0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement LearningCode1
Accelerating Primal-dual Methods for Regularized Markov Decision Processes—0
CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories—0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
Rule Mining over Knowledge Graphs via Reinforcement Learning—0
Selective Credit Assignment—0
PooL: Pheromone-inspired Communication Framework forLarge Scale Multi-Agent Reinforcement Learning—0
Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion—0
Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic—0
Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training—0
Shaping Advice in Deep Reinforcement LearningCode0
Transformation Coding: Simple Objectives for Equivariant Representations—0
TransDreamer: Reinforcement Learning with Transformer World Models—0
A Behavior Regularized Implicit Policy for Offline Reinforcement Learning—0
Can Interpretable Reinforcement Learning Manage Prosperity Your Way?—0
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices—0
UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations—0
VRL3: A Data-Driven Framework for Visual Deep Reinforcement LearningCode2
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization—0
CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban DrivingCode1
A Survey on Deep Reinforcement Learning-based Approaches for Adaptation and Generalization—0
Improving Intrinsic Exploration with Language Abstractions—0
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs—0
A Survey of Explainable Reinforcement Learning—0
Retrieval-Augmented Reinforcement Learning—0
Should I send this notification? Optimizing push notifications decision making by modeling the future—0
Robust Reinforcement Learning via Genetic Curriculum—0
Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight ControlCode1
Open-Ended Reinforcement Learning with Neural Reward FunctionsCode1
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo—0
Branching Reinforcement Learning—0
Domain Adaptive Fake News Detection via Reinforcement Learning—0
An Intrusion Response System utilizing Deep Q-Networks and System PartitionsCode0
Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle—0
Energy-Efficient Parking Analytics System using Deep Reinforcement LearningCode0
Safe Reinforcement Learning by Imagining the Near FutureCode1
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Benchmark Results

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