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 4311–4320 of 15113 papers

TitleStatusHype
LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement LearningCode0
Social Interpretable Reinforcement Learning—0
On the Limitations of Markovian Rewards to Express Multi-Objective, Risk-Sensitive, and Modal Tasks—0
Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement LearningCode0
Hierarchical Continual Reinforcement Learning via Large Language Model—0
Learning fast changing slow in spiking neural networks—0
Learning-based sensing and computing decision for data freshness in edge computing-enabled networks—0
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation—0
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research—0
Sample Efficient Reinforcement Learning by Automatically Learning to Compose Subtasks—0
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Benchmark Results

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