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 1776–1800 of 15113 papers

TitleStatusHype
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior RegularizationCode1
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point CloudsCode1
Learning Rewards from Linguistic FeedbackCode1
Learning to swim in potential flowCode1
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement LearningCode1
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
Neurosymbolic Reinforcement Learning with Formally Verified ExplorationCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks and Autoregressive Policy DecompositionCode1
Deep Reinforcement Learning for Process SynthesisCode1
CertRL: Formalizing Convergence Proofs for Value and Policy Iteration in CoqCode1
RL STaR Platform: Reinforcement Learning for Simulation based Training of RobotsCode1
SREC: Proactive Self-Remedy of Energy-Constrained UAV-Based Networks via Deep Reinforcement LearningCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
Finding Effective Security Strategies through Reinforcement Learning and Self-PlayCode1
Text Generation by Learning from DemonstrationsCode1
Meta-AAD: Active Anomaly Detection with Deep Reinforcement LearningCode1
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile NetworksCode1
Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware DetectionCode1
Reinforcement Learning for Optimal Primary Frequency Control: A Lyapunov ApproachCode1
DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous ControlCode1
Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement LearningCode1
Phasic Policy GradientCode1
Deep Active Inference for Partially Observable MDPsCode1
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Benchmark Results

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