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 3041–3050 of 15113 papers

TitleStatusHype
Joint Band Assignment and Beam Management using Hierarchical Reinforcement Learning for Multi-Band Communication—0
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities—0
MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement LearningCode0
Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car—0
Bayesian Exploration Networks—0
Reinforcement learning informed evolutionary search for autonomous systems testing—0
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory—0
Continuous Reinforcement Learning-based Dynamic Difficulty Adjustment in a Visual Working Memory Game—0
Conditional Kernel Imitation Learning for Continuous State Environments—0
RamseyRL: A Framework for Intelligent Ramsey Number Counterexample SearchingCode0
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Benchmark Results

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