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 2301–2310 of 15113 papers

TitleStatusHype
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation—0
The Fallacy of Minimizing Cumulative Regret in the Sequential Task Setting—0
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial GamesCode1
Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC—0
ViSaRL: Visual Reinforcement Learning Guided by Human Saliency—0
Neural-Kernel Conditional Mean Embeddings—0
Horizon-Free Regret for Linear Markov Decision Processes—0
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement LearningCode0
Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning—0
Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionCode2
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Benchmark Results

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