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 2801–2810 of 15113 papers

TitleStatusHype
Mining-Gym: A Configurable RL Benchmarking Environment for Truck Dispatch SchedulingCode0
AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models—0
Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation—0
Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning—0
RLCAD: Reinforcement Learning Training Gym for Revolution Involved CAD Command Sequence Generation—0
Sample-Efficient Reinforcement Learning of Koopman eNMPC—0
Evolutionary Policy Optimization—0
Adaptive Multi-Fidelity Reinforcement Learning for Variance Reduction in Engineering Design Optimization—0
Optimizing Navigation And Chemical Application in Precision Agriculture With Deep Reinforcement Learning And Conditional Action Tree—0
ViVa: Video-Trained Value Functions for Guiding Online RL from Diverse Data—0
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Benchmark Results

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