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 6726–6750 of 15113 papers

TitleStatusHype
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