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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2331–2340 of 15113 papers

TitleStatusHype
Bayesian Linear Regression on Deep Representations—0
Adaptive Structural Hyper-Parameter Configuration by Q-Learning—0
Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models—0
Bayesian Inference of Self-intention Attributed by Observer—0
Configuration Path Control—0
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning—0
Bayesian Hierarchical Reinforcement Learning—0
Analog Circuit Design with Dyna-Style Reinforcement Learning—0
Bayesian Exploration Networks—0
Bayesian Exploration for Lifelong Reinforcement Learning—0
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Benchmark Results

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