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 2921–2930 of 15113 papers

TitleStatusHype
AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks—0
Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles—0
AI Assisted Annotator using Reinforcement Learning—0
Adaptive Batch Size for Safe Policy Gradients—0
Attacking and Defending Deep Reinforcement Learning Policies—0
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving—0
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning—0
A* Tree Search for Portfolio Management—0
ACECODER: Acing Coder RL via Automated Test-Case Synthesis—0
A Hysteretic Q-learning Coordination Framework for Emerging Mobility Systems in Smart Cities—0
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Benchmark Results

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