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 1261–1270 of 15113 papers

TitleStatusHype
GraCo -- A Graph Composer for Integrated Circuits—0
Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problemsCode0
Natural Language Reinforcement LearningCode2
Model Checking for Reinforcement Learning in Autonomous Driving: One Can Do More Than You Think!—0
Multi-Agent Environments for Vehicle Routing ProblemsCode1
A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback—0
Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning—0
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning—0
LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog CircuitsCode1
ACING: Actor-Critic for Instruction Learning in Black-Box Large Language ModelsCode0
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Benchmark Results

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