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 6701–6750 of 15113 papers

TitleStatusHype
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration—0
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents—0
Weighted model estimation for offline model-based reinforcement learning—0
Regularized Softmax Deep Multi-Agent Q-LearningCode1
Offline Model-based Adaptable Policy LearningCode1
BooVI: Provably Efficient Bootstrapped Value Iteration—0
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculumCode0
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning—0
Curriculum Offline Imitating Learning—0
BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce MarketCode1
Counterexample Guided RL Policy Refinement Using Bayesian OptimizationCode0
Distributionally Robust Imitation Learning—0
Explicable Reward Design for Reinforcement Learning AgentsCode0
EDGE: Explaining Deep Reinforcement Learning PoliciesCode1
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement LearningCode1
Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning—0
Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning—0
Model-Free μ Synthesis via Adversarial Reinforcement Learning—0
The Power of Communication in a Distributed Multi-Agent System—0
MAMRL: Exploiting Multi-agent Meta Reinforcement Learning in WAN Traffic Engineering—0
Continuous Control With Ensemble Deep Deterministic Policy GradientsCode0
DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks—0
Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning—0
Reinforcement Learning Algorithm for Traffic Steering in Heterogeneous Network—0
Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement LearningCode0
Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware ImpairmentsCode0
Pessimistic Model Selection for Offline Deep Reinforcement Learning—0
How Can Creativity Occur in Multi-Agent Systems?—0
Final Adaptation Reinforcement Learning for N-Player Games—0
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions—0
Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning—0
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion DetectionCode0
Learning Long-Term Reward Redistribution via Randomized Return DecompositionCode1
Reinforcement Explanation Learning—0
Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning—0
Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings—0
Towards Modularity Optimization Using Reinforcement Learning to Community Detection in Dynamic Social Networks—0
DeepWiVe: Deep-Learning-Aided Wireless Video Transmission—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning—0
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning—0
Learning State Representations via Retracing in Reinforcement LearningCode0
How does AI play football? An analysis of RL and real-world football strategies—0
GDI: Rethinking What Makes Reinforcement Learning Different from Supervised Learning—0
A note on stabilizing reinforcement learning—0
Adaptively Calibrated Critic Estimates for Deep Reinforcement LearningCode0
A Review for Deep Reinforcement Learning in Atari: Benchmarks, Challenges, and Solutions—0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes—0
Application of Multi-Agent Reinforcement Learning for Battery Management in Renewable Mini-Grids—0
SatNet: A Benchmark for Satellite Scheduling Optimization—0
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Benchmark Results

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