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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4351–4375 of 15113 papers

TitleStatusHype
Understanding the Safety Requirements for Learning-based Power Systems OperationsCode0
Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware ImpairmentsCode0
Reinforcement learning based adaptive metaheuristicsCode0
Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning UpdatesCode0
Underwater Soft Fin Flapping Motion with Deep Neural Network Based Surrogate ModelCode0
Playing 2048 With Reinforcement LearningCode0
Online Prototype Alignment for Few-shot Policy TransferCode0
Unified Distributed EnvironmentCode0
QFlip: An Adaptive Reinforcement Learning Strategy for the FlipIt Security GameCode0
Unified Off-Policy Learning to Rank: a Reinforcement Learning PerspectiveCode0
Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR NetworksCode0
Lusifer: LLM-based User SImulated Feedback Environment for online Recommender systemsCode0
Unified State Representation Learning under Data AugmentationCode0
SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement LearningCode0
Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint ReplayCode0
Unifying Count-Based Exploration and Intrinsic MotivationCode0
Unifying Interpretability and Explainability for Alzheimer's Disease Progression PredictionCode0
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement LearningCode0
Playing Atari with Six NeuronsCode0
Playing Doom with SLAM-Augmented Deep Reinforcement LearningCode0
Model-based Lifelong Reinforcement Learning with Bayesian ExplorationCode0
Universally Expressive Communication in Multi-Agent Reinforcement LearningCode0
Universal Policies to Learn Them AllCode0
Universal Reinforcement Learning Algorithms: Survey and ExperimentsCode0
Universal Successor Features ApproximatorsCode0
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Benchmark Results

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