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 711–720 of 15113 papers

TitleStatusHype
RLCAD: Reinforcement Learning Training Gym for Revolution Involved CAD Command Sequence Generation—0
SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the WildCode7
Sample-Efficient Reinforcement Learning of Koopman eNMPC—0
Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation—0
Adaptive Multi-Fidelity Reinforcement Learning for Variance Reduction in Engineering Design Optimization—0
Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization—0
ViVa: Video-Trained Value Functions for Guiding Online RL from Diverse Data—0
Surrogate Learning in Meta-Black-Box Optimization: A Preliminary StudyCode2
Optimizing Navigation And Chemical Application in Precision Agriculture With Deep Reinforcement Learning And Conditional Action Tree—0
ComfyGPT: A Self-Optimizing Multi-Agent System for Comprehensive ComfyUI Workflow Generation—0
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Benchmark Results

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