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 3521–3530 of 15113 papers

TitleStatusHype
Adversary Agnostic Robust Deep Reinforcement Learning—0
Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense—0
Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks—0
Deep Reinforcement Learning-based Anti-jamming Power Allocation in a Two-cell NOMA Network—0
Deep Reinforcement Learning-based Authentic Dialogue Generation To Protect Youth From Cybergrooming—0
Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks—0
Deep Reinforcement Learning based Blind mmWave MIMO Beam Alignment—0
Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks—0
Deep Reinforcement Learning Based Controller for Active Heave Compensation—0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning—0
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Benchmark Results

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