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 911–920 of 15113 papers

TitleStatusHype
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum GamesCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Discovering General Reinforcement Learning Algorithms with Adversarial Environment DesignCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Gradient Imitation Reinforcement Learning for Low Resource Relation ExtractionCode1
Discrete Codebook World Models for Continuous ControlCode1
Show:102550
← PrevPage 92 of 1512Next →

Benchmark Results

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