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 7301–7350 of 15113 papers

TitleStatusHype
Learning Relative Return Policies With Upside-Down Reinforcement Learning—0
Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors—0
Comparative analysis of machine learning methods for active flow control—0
Consistent Dropout for Policy Gradient Reinforcement Learning—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four—0
Reinforcement Learning from Demonstrations by Novel Interactive Expert and Application to Automatic Berthing Control Systems for Unmanned Surface Vessel—0
Multi-fidelity reinforcement learning framework for shape optimization—0
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning—0
Reward-Free Policy Space Compression for Reinforcement Learning—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
Continual Auxiliary Task Learning—0
A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning—0
Behaviour-neutral Smart Charging of Plugin Electric Vehicles: Reinforcement learning approach—0
Autonomous Warehouse Robot using Deep Q-Learning—0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories—0
Accelerating Primal-dual Methods for Regularized Markov Decision Processes—0
Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks—0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Rule Mining over Knowledge Graphs via Reinforcement Learning—0
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing—0
PooL: Pheromone-inspired Communication Framework forLarge Scale Multi-Agent Reinforcement Learning—0
Selective Credit Assignment—0
Shaping Advice in Deep Reinforcement LearningCode0
TransDreamer: Reinforcement Learning with Transformer World Models—0
Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion—0
Transformation Coding: Simple Objectives for Equivariant Representations—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
A Behavior Regularized Implicit Policy for Offline Reinforcement Learning—0
Can Interpretable Reinforcement Learning Manage Prosperity Your Way?—0
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
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
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization—0
A Survey on Deep Reinforcement Learning-based Approaches for Adaptation and Generalization—0
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs—0
Improving Intrinsic Exploration with Language Abstractions—0
A Survey of Explainable Reinforcement Learning—0
An Intrusion Response System utilizing Deep Q-Networks and System PartitionsCode0
Branching Reinforcement Learning—0
Domain Adaptive Fake News Detection via Reinforcement Learning—0
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo—0
User-Oriented Robust Reinforcement Learning—0
Interpretable Reinforcement Learning with Multilevel Subgoal Discovery—0
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement LearningCode0
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Benchmark Results

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