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 861–870 of 15113 papers

TitleStatusHype
Compile Scene Graphs with Reinforcement LearningCode1
Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk DecodingCode1
Discriminator Soft Actor Critic without Extrinsic RewardsCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
f-IRL: Inverse Reinforcement Learning via State Marginal MatchingCode1
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline GenerationCode1
Concise Reasoning via Reinforcement LearningCode1
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning TechniquesCode1
Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement LearningCode1
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Benchmark Results

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