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 7376–7400 of 15113 papers

TitleStatusHype
Lyapunov-based uncertainty-aware safe reinforcement learning—0
Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning—0
Lyapunov Robust Constrained-MDPs: Soft-Constrained Robustly Stable Policy Optimization under Model Uncertainty—0
Lyceum: An efficient and scalable ecosystem for robot learning—0
M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Scheduling—0
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning—0
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning—0
MACC: Cross-Layer Multi-Agent Congestion Control with Deep Reinforcement Learning—0
Machine Learning aided Crop Yield Optimization—0
Machine learning and control engineering: The model-free case—0
Machine Learning Applications in the Routing in Computer Networks—0
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
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Benchmark Results

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