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 3101–3110 of 15113 papers

TitleStatusHype
RbRL2.0: Integrated Reward and Policy Learning for Rating-based Reinforcement Learning—0
Combining LLM decision and RL action selection to improve RL policy for adaptive interventions—0
Average Reward Reinforcement Learning for Wireless Radio Resource Management—0
Pareto Set Learning for Multi-Objective Reinforcement Learning—0
DRDT3: Diffusion-Refined Decision Test-Time Training Model—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
Diffusion Models for Smarter UAVs: Decision-Making and Modeling—0
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Benchmark Results

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