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 1071–1080 of 15113 papers

TitleStatusHype
DRDT3: Diffusion-Refined Decision Test-Time Training Model—0
Average Reward Reinforcement Learning for Wireless Radio Resource Management—0
Pareto Set Learning for Multi-Objective Reinforcement Learning—0
An Empirical Study of Deep Reinforcement Learning in Continuing TasksCode0
AlgoPilot: Fully Autonomous Program Synthesis Without Human-Written Programs—0
A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement LearningCode0
Hierarchical Reinforcement Learning for Optimal Agent Grouping in Cooperative Systems—0
Smart Imitator: Learning from Imperfect Clinical DecisionsCode0
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing—0
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingCode1
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Benchmark Results

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