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 1921–1930 of 15113 papers

TitleStatusHype
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning—0
When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal AbstractionsCode0
RILe: Reinforced Imitation Learning—0
Unifying Interpretability and Explainability for Alzheimer's Disease Progression PredictionCode0
Enhanced Gene Selection in Single-Cell Genomics: Pre-Filtering Synergy and Reinforced Optimization—0
Integrating Domain Knowledge for handling Limited Data in Offline RL—0
Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning—0
Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment—0
Hybrid Reinforcement Learning from Offline Observation Alone—0
CHARME: A chain-based reinforcement learning approach for the minor embedding problem—0
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Benchmark Results

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