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 621–630 of 15113 papers

TitleStatusHype
Better Decisions through the Right Causal World Model—0
Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement LearningCode1
TW-CRL: Time-Weighted Contrastive Reward Learning for Efficient Inverse Reinforcement Learning—0
Trust-Region Twisted Policy ImprovementCode0
Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning IncentivizationCode2
Stratified Expert Cloning with Adaptive Selection for User Retention in Large-Scale Recommender Systems—0
xMTF: A Formula-Free Model for Reinforcement-Learning-Based Multi-Task Fusion in Recommender Systems—0
Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models—0
The Role of Environment Access in Agnostic Reinforcement Learning—0
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning—0
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Benchmark Results

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