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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3471–3480 of 15113 papers

TitleStatusHype
Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems—0
Towards Measuring Goal-Directedness in AI Systems—0
DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL—0
Improved Off-policy Reinforcement Learning in Biological Sequence DesignCode0
Data-driven Under Frequency Load Shedding Using Reinforcement Learning—0
AdaMemento: Adaptive Memory-Assisted Policy Optimization for Reinforcement Learning—0
A Reinforcement Learning Engine with Reduced Action and State Space for Scalable Cyber-Physical Optimal Response—0
Improving Portfolio Optimization Results with Bandit NetworksCode0
Spatial-aware decision-making with ring attractors in reinforcement learning systems—0
Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping—0
Show:102550
← PrevPage 348 of 1512Next →

Benchmark Results

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