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 4131–4140 of 15113 papers

TitleStatusHype
ACTRCE: Augmenting Experience via Teacher’s Advice—0
ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning—0
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning—0
AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning—0
AdaCred: Adaptive Causal Decision Transformers with Feature Crediting—0
AdaMemento: Adaptive Memory-Assisted Policy Optimization for Reinforcement Learning—0
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps—0
Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation—0
adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems—0
AdaPool: A Diurnal-Adaptive Fleet Management Framework using Model-Free Deep Reinforcement Learning and Change Point Detection—0
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Benchmark Results

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