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 2401–2410 of 15113 papers

TitleStatusHype
Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation—0
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning—0
A MultiModal Social Robot Toward Personalized Emotion Interaction—0
A Multimodal Learning-based Approach for Autonomous Landing of UAV—0
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning—0
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization—0
A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies—0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
BANANAS: Bayesian Optimization with Neural Networks for Neural Architecture Search—0
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization—0
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Benchmark Results

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