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 3126–3150 of 15113 papers

TitleStatusHype
A New Interpretation of the Certainty-Equivalence Approach for PAC Reinforcement Learning with a Generative Model—0
Representation Convergence: Mutual Distillation is Secretly a Form of RegularizationCode0
SR-Reward: Taking The Path More Traveled—0
Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning—0
On the Statistical Complexity for Offline and Low-Adaptive Reinforcement Learning with Structures—0
Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement LearningCode0
A Graphical Approach to State Variable Selection in Off-policy Learning—0
RaSS: Improving Denoising Diffusion Samplers with Reinforced Active Sampling Scheduler—0
Hybridising Reinforcement Learning and Heuristics for Hierarchical Directed Arc Routing ProblemsCode0
Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning—0
Towards Unraveling and Improving Generalization in World Models—0
FORM: Learning Expressive and Transferable First-Order Logic Reward Machines—0
Weber-Fechner Law in Temporal Difference learning derived from Control as Inference—0
UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI—0
An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework—0
Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret—0
Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey—0
Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques—0
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning—0
Efficient and Scalable Deep Reinforcement Learning for Mean Field Control GamesCode0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization—0
xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of ExplainabilityCode0
Provably Efficient Exploration in Reward Machines with Low Regret—0
A Reinforcement Learning-Based Task Mapping Method to Improve the Reliability of Clustered Manycores—0
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Benchmark Results

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