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 551–575 of 15113 papers

TitleStatusHype
Control-Oriented Model-Based Reinforcement Learning with Implicit DifferentiationCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning ApproachCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
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 Preference Learning: Learning from Human Feedback without RLCode1
Advancing Multimodal Reasoning via Reinforcement Learning with Cold StartCode1
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Contrastive Active InferenceCode1
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
Continuous control with deep reinforcement learningCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Continual Learning with Gated Incremental Memories for sequential data processingCode1
Contextualized Rewriting for Text SummarizationCode1
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement LearningCode1
Contextualize Me -- The Case for Context in Reinforcement LearningCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
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Benchmark Results

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