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 2481–2490 of 15113 papers

TitleStatusHype
Adaptive Q-Aid for Conditional Supervised Learning in Offline Reinforcement Learning—0
A Survey of Constraint Formulations in Safe Reinforcement Learning—0
Rethinking the Role of Proxy Rewards in Language Model AlignmentCode0
The Political Preferences of LLMs—0
The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models—0
An Auction-based Marketplace for Model Trading in Federated Learning—0
To the Max: Reinventing Reward in Reinforcement LearningCode0
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems—0
StepCoder: Improve Code Generation with Reinforcement Learning from Compiler FeedbackCode2
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation LearningCode0
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Benchmark Results

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