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 9101–9125 of 15113 papers

TitleStatusHype
PRewrite: Prompt Rewriting with Reinforcement Learning—0
Pricing commodity swing options—0
Primal-Dual Spectral Representation for Off-policy Evaluation—0
Primitive Agentic First-Order Optimization—0
Primitive Skill-based Robot Learning from Human Evaluative Feedback—0
Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF—0
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons—0
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation—0
Prioritized Guidance for Efficient Multi-Agent Reinforcement Learning Exploration—0
Prioritized offline Goal-swapping Experience Replay—0
Prioritized Sweeping Neural DynaQ with Multiple Predecessors, and Hippocampal Replays—0
Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning—0
Prior Preference Learning From Experts: Designing A Reward with Active Inference—0
Prior Preference Learning from Experts:Designing a Reward with Active Inference—0
Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning—0
PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations—0
Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning—0
Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning—0
Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning—0
Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning—0
Privacy Preserving Off-Policy Evaluation—0
Privacy-Preserving Reinforcement Learning Beyond Expectation—0
Privacy Preserving Reinforcement Learning for Population Processes—0
Privacy-Preserving Kickstarting Deep Reinforcement Learning with Privacy-Aware Learners—0
Privately Aligning Language Models with Reinforcement Learning—0
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Benchmark Results

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