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 2676–2700 of 15113 papers

TitleStatusHype
Interval timing in deep reinforcement learning agentsCode0
Intrinsic Rewards from Self-Organizing Feature Maps for Exploration in Reinforcement LearningCode0
A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement LearningCode0
A Threshold-based Scheme for Reinforcement Learning in Neural NetworksCode0
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement LearningCode0
Interactive Query-Assisted Summarization via Deep Reinforcement LearningCode0
Towards Abstractive Timeline Summarisation using Preference-based Reinforcement LearningCode0
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and LimitationsCode0
Intelligent Trainer for Model-Based Reinforcement LearningCode0
A Temporal Difference Method for Stochastic Continuous DynamicsCode0
Interactive Learning from Activity DescriptionCode0
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex TasksCode0
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning AlgorithmsCode0
Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space -- Fundamental Theory and MethodsCode0
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning AlgorithmsCode0
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving GeneralizationCode0
Accurate Uncertainties for Deep Learning Using Calibrated RegressionCode0
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic EnvironmentsCode0
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from ImagesCode0
A Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement LearningCode0
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
Insights From the NeurIPS 2021 NetHack ChallengeCode0
Inherently Explainable Reinforcement Learning in Natural LanguageCode0
Input Convex Neural NetworksCode0
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement LearningCode0
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Benchmark Results

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