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 6401–6450 of 15113 papers

TitleStatusHype
Diagnosing Reinforcement Learning for Traffic Signal Control—0
Dialog Action-Aware Transformer for Dialog Policy Learning—0
Dialogue Evaluation with Offline Reinforcement Learning—0
Dialogue manager domain adaptation using Gaussian process reinforcement learning—0
Dialogue Shaping: Empowering Agents through NPC Interaction—0
DiBB: Distributing Black-Box Optimization—0
Dichotomy of Control: Separating What You Can Control from What You Cannot—0
Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning—0
Difference of Convex Functions Programming Applied to Control with Expert Data—0
Difference of Convex Functions Programming for Reinforcement Learning—0
Difference Rewards Policy Gradients—0
Differentiable Arbitrating in Zero-sum Markov Games—0
Differentiable Discrete Event Simulation for Queuing Network Control—0
Differentiable Logic Machines—0
Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning—0
Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning—0
Differentially Private Exploration in Reinforcement Learning with Linear Representation—0
Differentially Private Deep Model-Based Reinforcement Learning—0
Differentially Private Policy Evaluation—0
Differentially Private Reinforcement Learning with Linear Function Approximation—0
Differential Variable Speed Limits Control for Freeway Recurrent Bottlenecks via Deep Reinforcement learning—0
Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks—0
DiffPoGAN: Diffusion Policies with Generative Adversarial Networks for Offline Reinforcement Learning—0
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools—0
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching—0
Diffused Task-Agnostic Milestone Planner—0
Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task—0
Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses—0
Diffusion Models for Smarter UAVs: Decision-Making and Modeling—0
Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning—0
Diffusion Self-Weighted Guidance for Offline Reinforcement Learning—0
Diffusion Spectral Representation for Reinforcement Learning—0
Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning—0
Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks—0
Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling—0
Digital Twin Assisted Risk-Aware Sleep Mode Management Using Deep Q-Networks—0
Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications—0
Digital Twin-Native AI-Driven Service Architecture for Industrial Networks—0
DiGrad: Multi-Task Reinforcement Learning with Shared Actions—0
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation—0
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking—0
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning—0
DINASTI: Dialogues with a Negotiating Appointment Setting Interface—0
DIP-R1: Deep Inspection and Perception with RL Looking Through and Understanding Complex Scenes—0
DIP-RL: Demonstration-Inferred Preference Learning in Minecraft—0
Direct and indirect reinforcement learning—0
Directed Exploration for Reinforcement Learning—0
Directed Exploration in PAC Model-Free Reinforcement Learning—0
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice—0
Revisiting a Design Choice in Gradient Temporal Difference Learning—0
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Benchmark Results

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