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 1491–1500 of 15113 papers

TitleStatusHype
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement LearningCode1
Solving robust MDPs as a sequence of static RL problems—0
Solving Multi-Goal Robotic Tasks with Decision Transformer—0
Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards—0
Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems—0
AlphaRouter: Quantum Circuit Routing with Reinforcement Learning and Tree Search—0
Towards Measuring Goal-Directedness in AI Systems—0
GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systemsCode1
Data-driven Under Frequency Load Shedding Using Reinforcement Learning—0
Improved Off-policy Reinforcement Learning in Biological Sequence DesignCode0
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Benchmark Results

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