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 1641–1650 of 15113 papers

TitleStatusHype
Bayesian Generational Population-Based TrainingCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Behavior Proximal Policy OptimizationCode1
Dynamic Sparse Training for Deep Reinforcement LearningCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGymCode1
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate ProgressCode1
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich FeaturesCode1
Goal-Conditioned Generators of Deep PoliciesCode1
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Benchmark Results

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