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 626–650 of 15113 papers

TitleStatusHype
Actor-Critic Reinforcement Learning for Control with Stability GuaranteeCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Accelerating Reinforcement Learning with Learned Skill PriorsCode1
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
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
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Continual Learning with Gated Incremental Memories for sequential data processingCode1
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
Contextualize Me -- The Case for Context in Reinforcement LearningCode1
Contextualized Rewriting for Text SummarizationCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
Actor-Attention-Critic for Multi-Agent Reinforcement LearningCode1
Accelerating Quadratic Optimization with Reinforcement LearningCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
Constructions in combinatorics via neural networksCode1
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Benchmark Results

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