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

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Showing 6551–6600 of 15113 papers

TitleStatusHype
A deep reinforcement learning model for predictive maintenance planning of road assets: Integrating LCA and LCCA—0
Variational Quantum Soft Actor-CriticCode1
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning—0
Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units—0
Interpretable Preference-based Reinforcement Learning with Tree-Structured Reward Functions—0
Differentially Private Regret Minimization in Episodic Markov Decision ProcessesCode0
AGPNet -- Autonomous Grading Policy Network—0
Exploration-exploitation trade-off for continuous-time episodic reinforcement learning with linear-convex models—0
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment—0
Masked Deep Q-Recommender for Effective Question Scheduling—0
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal controlCode1
Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehicles—0
Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement LearningCode0
Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading—0
Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning—0
Space Non-cooperative Object Active Tracking with Deep Reinforcement LearningCode1
Unsupervised Compressive Text Summarisation with Reinforcement Learning—0
Deep Reinforcement Learning-based Authentic Dialogue Generation To Protect Youth From Cybergrooming—0
Personalized Lane Change Decision Algorithm Using Deep Reinforcement Learning Approach—0
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents—0
Learning Reward Machines: A Study in Partially Observable Reinforcement Learning—0
Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes (Technical Report)Code0
Symmetry-aware Neural Architecture for Embodied Visual Navigation—0
Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation—0
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning—0
Learning to Share in Multi-Agent Reinforcement LearningCode1
Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs—0
Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation—0
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning—0
Inherently Explainable Reinforcement Learning in Natural LanguageCode0
Unsupervised Reinforcement Learning in Multiple EnvironmentsCode0
DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities—0
Feature-Attending Recurrent Modules for Generalization in Reinforcement LearningCode0
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning—0
Programmatic Reward Design by Example—0
Representation and Invariance in Reinforcement Learning—0
Reinforcing Semantic-Symmetry for Document Summarization—0
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study—0
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning—0
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement LearningCode0
Conjugated Discrete Distributions for Distributional Reinforcement LearningCode0
Quantifying Multimodality in World Models—0
Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning—0
Meta-CPR: Generalize to Unseen Large Number of Agents with Communication Pattern Recognition Module—0
Stochastic Actor-Executor-Critic for Image-to-Image TranslationCode1
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image RegistrationCode1
Human-Level Control through Directly-Trained Deep Spiking Q-NetworksCode1
Continual Learning In Environments With Polynomial Mixing TimesCode0
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Benchmark Results

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