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 3871–3880 of 15113 papers

TitleStatusHype
CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Models—0
ACTRCE: Augmenting Experience via Teacher’s Advice—0
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT—0
Deep Surrogate Assisted Generation of Environments—0
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation—0
Bad-Policy Density: A Measure of Reinforcement Learning Hardness—0
Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning—0
DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning—0
Assessment of Reward Functions in Reinforcement Learning for Multi-Modal Urban Traffic Control under Real-World limitations—0
A General Perspective on Objectives of Reinforcement Learning—0
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Benchmark Results

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