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 7776–7800 of 15113 papers

TitleStatusHype
Pessimistic Model Selection for Offline Deep Reinforcement Learning—0
Reinforcement Learning Algorithm for Traffic Steering in Heterogeneous Network—0
Multi-UAV Conflict Resolution with Graph Convolutional 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
DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks—0
Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning—0
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion DetectionCode0
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
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning—0
DeepWiVe: Deep-Learning-Aided Wireless Video Transmission—0
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
Application of Multi-Agent Reinforcement Learning for Battery Management in Renewable Mini-Grids—0
Adaptively Calibrated Critic Estimates for Deep Reinforcement LearningCode0
A Review for Deep Reinforcement Learning in Atari: Benchmarks, Challenges, and Solutions—0
A note on stabilizing reinforcement learning—0
Learning to Schedule Heuristics for the Simultaneous Stochastic Optimization of Mining Complexes—0
GDI: Rethinking What Makes Reinforcement Learning Different from Supervised Learning—0
How does AI play football? An analysis of RL and real-world football strategies—0
Learning State Representations via Retracing in Reinforcement LearningCode0
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Benchmark Results

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