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 2901–2910 of 15113 papers

TitleStatusHype
AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms—0
Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication—0
Attention-based QoE-aware Digital Twin Empowered Edge Computing for Immersive Virtual Reality—0
AI-driven materials design: a mini-review—0
Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks—0
Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems—0
AAMDRL: Augmented Asset Management with Deep Reinforcement Learning—0
Attention-based Deep Reinforcement Learning for Multi-view Environments—0
AI-based traffic analysis in digital twin networks—0
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States—0
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Benchmark Results

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