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 3811–3820 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning with Decorrelation—0
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization—0
Avoiding Negative Side-Effects and Promoting Safe Exploration with Imaginative Planning—0
Deep Reinforcement Learning With Adaptive Combined Critics—0
Deep Reinforcement Learning with Adjustments—0
Avoiding Wireheading with Value Reinforcement Learning—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing—0
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning—0
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework—0
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Benchmark Results

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