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 2851–2860 of 15113 papers

TitleStatusHype
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Vision-Language Models are Zero-Shot Reward Models for Reinforcement LearningCode1
SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models—0
Towards Robust Offline Reinforcement Learning under Diverse Data CorruptionCode1
Learning to Optimise Climate Sensor Placement using a Transformer—0
Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning—0
Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement LearningCode0
On The Expressivity of Objective-Specification Formalisms in Reinforcement Learning—0
Improving Generalization of Alignment with Human Preferences through Group Invariant Learning—0
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven OptimizationCode1
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Benchmark Results

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