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 2321–2330 of 15113 papers

TitleStatusHype
(N,K)-Puzzle: A Cost-Efficient Testbed for Benchmarking Reinforcement Learning Algorithms in Generative Language Model—0
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts—0
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation—0
Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning—0
Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning—0
RLingua: Improving Reinforcement Learning Sample Efficiency in Robotic Manipulations With Large Language Models—0
In-context Exploration-Exploitation for Reinforcement Learning—0
Distributional Successor Features Enable Zero-Shot Policy Optimization—0
Enhancing Classification Performance via Reinforcement Learning for Feature Selection—0
PEaRL: Personalized Privacy of Human-Centric Systems using Early-Exit Reinforcement Learning—0
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Benchmark Results

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