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 6751–6800 of 15113 papers

TitleStatusHype
Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation—0
On the (In)Tractability of Reinforcement Learning for LTL Objectives—0
Reversible Action Design for Combinatorial Optimization with ReinforcementLearning—0
Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy—0
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning—0
Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks—0
Inducing Functions through Reinforcement Learning without Task Specification—0
Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS—0
Independent Learning in Stochastic Games—0
Generating GPU Compiler Heuristics using Reinforcement Learning—0
Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation—0
Real-World Dexterous Object Manipulation based Deep Reinforcement LearningCode0
Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures—0
Reinforcement Learning for Few-Shot Text Generation AdaptationCode0
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor RectificationCode1
UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning—0
Off-Policy Correction For Multi-Agent Reinforcement LearningCode0
An application of reinforcement learning to residential energy storage under real-time pricing—0
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning—0
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming—0
Reinforcement Learning with General LTL Objectives is Intractable—0
Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning—0
Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning—0
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation—0
Towards Safe, Explainable, and Regulated Autonomous Driving—0
Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks—0
HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments—0
Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs—0
An Improved Reinforcement Learning Model Based on Sentiment Analysis—0
Learn Quasi-stationary Distributions of Finite State Markov Chain—0
Machine Learning for Mechanical Ventilation Control (Extended Abstract)—0
Uncertainty-aware Low-Rank Q-Matrix Estimation for Deep Reinforcement Learning—0
Reinforcement Learning with Adaptive Curriculum Dynamics Randomization for Fault-Tolerant Robot Control—0
Generalized Decision Transformer for Offline Hindsight Information MatchingCode1
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning—0
Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines—0
Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI TeammatesCode0
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement LearningCode0
Self-Learning Tuning for Post-Silicon Validation—0
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition—0
Post-processing Networks: A Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement Learning—0
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems—0
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems—0
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization—0
MAD for Robust Reinforcement Learning in Machine Translation—0
Probing the Robustness of Trained Metrics for Conversational Dialogue Systems—0
Deep Reinforcement Learning for Entity Alignment—0
Improving Learning from Demonstrations by Learning from Experience—0
Compressive Features in Offline Reinforcement Learning for Recommender Systems—0
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Benchmark Results

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