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 7801–7850 of 15113 papers

TitleStatusHype
On the (In)Tractability of Reinforcement Learning for LTL Objectives—0
SatNet: A Benchmark for Satellite Scheduling Optimization—0
Reversible Action Design for Combinatorial Optimization with ReinforcementLearning—0
Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation—0
Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy—0
Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks—0
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning—0
Generating GPU Compiler Heuristics using Reinforcement Learning—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
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
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
UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning—0
Off-Policy Correction For Multi-Agent Reinforcement LearningCode0
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
Reinforcement Learning with General LTL Objectives is Intractable—0
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming—0
Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs—0
HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments—0
Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks—0
Towards Safe, Explainable, and Regulated Autonomous Driving—0
Reinforcement Learning with Adaptive Curriculum Dynamics Randomization for Fault-Tolerant Robot Control—0
Machine Learning for Mechanical Ventilation Control (Extended Abstract)—0
Uncertainty-aware Low-Rank Q-Matrix Estimation for Deep Reinforcement Learning—0
Learn Quasi-stationary Distributions of Finite State Markov Chain—0
An Improved Reinforcement Learning Model Based on Sentiment Analysis—0
Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines—0
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning—0
Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI TeammatesCode0
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement LearningCode0
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition—0
Self-Learning Tuning for Post-Silicon Validation—0
Route Optimization via Environment-Aware Deep Network and Reinforcement Learning—0
MAD for Robust Reinforcement Learning in Machine Translation—0
Post-processing Networks: A Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement Learning—0
Reinforcement Learning with Feedback from Multiple Humans with Diverse Skills—0
Probing the Robustness of Trained Metrics for Conversational Dialogue Systems—0
Improving Learning from Demonstrations by Learning from Experience—0
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization—0
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems—0
Compressive Features in Offline Reinforcement Learning for Recommender Systems—0
Causal policy ranking—0
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems—0
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Benchmark Results

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