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 2201–2225 of 15113 papers

TitleStatusHype
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Cross-Domain Policy Adaptation by Capturing Representation MismatchCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
Continual Learning with Gated Incremental Memories for sequential data processingCode1
B-Pref: Benchmarking Preference-Based Reinforcement LearningCode1
Reinforcement Learning in High-frequency Market MakingCode1
Bridging Imagination and Reality for Model-Based Deep Reinforcement LearningCode1
Continuous control with deep reinforcement learningCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement LearningCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Maximum Entropy-Regularized Multi-Goal Reinforcement LearningCode1
Enhancing Efficiency and Exploration in Reinforcement Learning for LLMsCode1
Contrastive Active InferenceCode1
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement LearningCode1
Learning Domain Invariant Representations in Goal-conditioned Block MDPsCode1
Learning Multi-Agent Communication through Structured Attentive ReasoningCode1
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Benchmark Results

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