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 651–675 of 15113 papers

TitleStatusHype
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Contrastive Active InferenceCode1
Active Reinforcement Learning for Robust Building ControlCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
AWAC: Accelerating Online Reinforcement Learning with Offline DatasetsCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Continual World: A Robotic Benchmark For Continual Reinforcement LearningCode1
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
Continuous control with deep reinforcement learningCode1
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
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
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
Constructions in combinatorics via neural networksCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement LearningCode1
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Benchmark Results

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