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 6126–6150 of 15113 papers

TitleStatusHype
RL-PGO: Reinforcement Learning-based Planar Pose-Graph OptimizationCode0
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions—0
Domain Knowledge-Based Automated Analog Circuit Design with Deep Reinforcement Learning—0
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option TemplatesCode0
Consolidated Adaptive T-soft Update for Deep Reinforcement Learning—0
Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search—0
Context-Hierarchy Inverse Reinforcement Learning—0
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach—0
Building a 3-Player Mahjong AI using Deep Reinforcement LearningCode1
Reachability analysis in stochastic directed graphs by reinforcement learning—0
Evolving-to-Learn Reinforcement Learning Tasks with Spiking Neural Networks—0
Learning Transferable Reward for Query Object Localization with Policy AdaptationCode0
Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing—0
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RLCode1
Quantum Deep Reinforcement Learning for Robot Navigation TasksCode0
Learning Relative Return Policies With Upside-Down Reinforcement Learning—0
Comparative analysis of machine learning methods for active flow control—0
Reinforcement Learning in Practice: Opportunities and Challenges—0
Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors—0
Blockchain Framework for Artificial Intelligence ComputationCode1
Consistent Dropout for Policy Gradient Reinforcement Learning—0
Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in IntralogisticsCode1
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement LearningCode1
Reinforcement Learning from Demonstrations by Novel Interactive Expert and Application to Automatic Berthing Control Systems for Unmanned Surface Vessel—0
Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four—0
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Benchmark Results

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