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 9451–9475 of 15113 papers

TitleStatusHype
Machine Learning Approaches For Motor Learning: A Short Review—0
Machine-learning based noise characterization and correction on neutral atoms NISQ devices—0
Machine Learning for Mechanical Ventilation Control (Extended Abstract)—0
Machine Learning in Event-Triggered Control: Recent Advances and Open Issues—0
Machine Teaching in Hierarchical Genetic Reinforcement Learning: Curriculum Design of Reward Functions for Swarm Shepherding—0
Machine Translation for Machines: the Sentiment Classification Use Case—0
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based summarisation—0
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based multi-document summarisation—0
Macro-Action-Based Deep Multi-Agent Reinforcement Learning—0
Macro-Action-Based Multi-Agent/Robot Deep Reinforcement Learning under Partial Observability—0
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder—0
MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design—0
MAD for Robust Reinforcement Learning in Machine Translation—0
MAD for Robust Reinforcement Learning in Machine Translation—0
MADRaS : Multi Agent Driving Simulator—0
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL—0
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning—0
MAG-GNN: Reinforcement Learning Boosted Graph Neural Network—0
MAGICS: Adversarial RL with Minimax Actors Guided by Implicit Critic Stackelberg for Convergent Neural Synthesis of Robot Safety—0
Magistral—0
Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning—0
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning—0
Maintaining cooperation in complex social dilemmas using deep reinforcement learning—0
Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks—0
Making Curiosity Explicit in Vision-based RL—0
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Benchmark Results

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