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 4351–4360 of 15113 papers

TitleStatusHype
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning—0
Adversarial joint attacks on legged robots—0
Adversarial Learning of Task-Oriented Neural Dialog Models—0
State-Conditioned Adversarial Subgoal Generation—0
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits—0
Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning—0
Adversarially Trained Neural Policies in the Fourier Domain—0
Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing—0
Adversarial Model for Offline Reinforcement Learning—0
Adversarial Radar Inference. From Inverse Tracking to Inverse Reinforcement Learning of Cognitive Radar—0
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Benchmark Results

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