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 2726–2750 of 15113 papers

TitleStatusHype
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from ImagesCode0
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex TasksCode0
Instance based Generalization in Reinforcement LearningCode0
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving GeneralizationCode0
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
Input Convex Neural NetworksCode0
Insights From the NeurIPS 2021 NetHack ChallengeCode0
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic EnvironmentsCode0
Information-Theoretic State Variable Selection for Reinforcement LearningCode0
A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open ProblemsCode0
Information-Driven Adaptive Sensing Based on Deep Reinforcement LearningCode0
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and ExperimentsCode0
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement LearningCode0
Inherently Explainable Reinforcement Learning in Natural LanguageCode0
Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space -- Fundamental Theory and MethodsCode0
Intelligent Traffic Light via Policy-based Deep Reinforcement LearningCode0
Influence-Based Multi-Agent ExplorationCode0
Influencing Reinforcement Learning through Natural Language GuidanceCode0
Influence-aware Memory Architectures for Deep Reinforcement LearningCode0
Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement LearningCode0
AgGym: An agricultural biotic stress simulation environment for ultra-precision management planningCode0
Increasing the Action Gap: New Operators for Reinforcement LearningCode0
Infinite Time Horizon Safety of Bayesian Neural NetworksCode0
Information-Directed Exploration for Deep Reinforcement LearningCode0
Incorporating Rivalry in Reinforcement Learning for a Competitive GameCode0
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Benchmark Results

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