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 641–650 of 15113 papers

TitleStatusHype
Sequential Planning in Large Partially Observable Environments guided by LLMsCode1
The Generalization Gap in Offline Reinforcement LearningCode1
Multi-Agent Reinforcement Learning via Distributed MPC as a Function ApproximatorCode1
UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal ControlCode1
Mitigating Open-Vocabulary Caption HallucinationsCode1
Harnessing Discrete Representations For Continual Reinforcement LearningCode1
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Unveiling the Implicit Toxicity in Large Language ModelsCode1
Large Language Model as a Policy Teacher for Training Reinforcement Learning AgentsCode1
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Benchmark Results

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