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 3701–3710 of 15113 papers

TitleStatusHype
Revolutionizing Genomics with Reinforcement Learning Techniques—0
Limited Query Graph Connectivity Test—0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors—0
Exponential Hardness of Reinforcement Learning with Linear Function Approximation—0
On Bellman's principle of optimality and Reinforcement learning for safety-constrained Markov decision process—0
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning—0
The Dormant Neuron Phenomenon in Deep Reinforcement LearningCode6
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification—0
EvoTorch: Scalable Evolutionary Computation in PythonCode3
Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization—0
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Benchmark Results

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