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 1291–1300 of 15113 papers

TitleStatusHype
To bootstrap or to rollout? An optimal and adaptive interpolation—0
Innate-Values-driven Reinforcement Learning based Cognitive Modeling—0
Towards Practical Deep Schedulers for Allocating Cellular Radio Resources—0
Robot See, Robot Do: Imitation Reward for Noisy Financial Environments—0
LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs—0
RLInspect: An Interactive Visual Approach to Assess Reinforcement Learning Algorithm—0
Recommender systems and reinforcement learning for human-building interaction and context-aware support: A text mining-driven review of scientific literatureCode0
TIPO: Text to Image with Text Presampling for Prompt OptimizationCode2
Doubly Mild Generalization for Offline Reinforcement LearningCode1
Robust Offline Reinforcement Learning for Non-Markovian Decision Processes—0
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Benchmark Results

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