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 811–820 of 15113 papers

TitleStatusHype
A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game—0
ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning—0
Vairiational Stochastic Games—0
Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security—0
Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation—0
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement LearningCode4
Policy Constraint by Only Support Constraint for Offline Reinforcement LearningCode0
Generative Multi-Agent Q-Learning for Policy Optimization: Decentralized Wireless Networks—0
Tractable Representations for Convergent Approximation of Distributional HJB Equations—0
Multi-Fidelity Policy Gradient Algorithms—0
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Benchmark Results

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