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 10176–10200 of 15113 papers

TitleStatusHype
Nested-Wasserstein Self-Imitation Learning for Sequence Generation—0
Network Defense is Not a Game—0
Networked Multi-Agent Reinforcement Learning with Emergent Communication—0
Network Resource Allocation Strategy Based on Deep Reinforcement Learning—0
Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning—0
Network Topology Optimization via Deep Reinforcement Learning—0
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning—0
Neural Algorithms for Graph Navigation—0
Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control—0
Neural architecture impact on identifying temporally extended Reinforcement Learning tasks—0
Neural Architecture Search in Embedding Space—0
Neural-based Control for CubeSat Docking Maneuvers—0
Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection—0
Neural Categorical Priors for Physics-Based Character Control—0
Neural Combinatorial Optimization with Reinforcement Learning : Solving theVehicle Routing Problem with Time Windows—0
Neural Coreference Resolution based on Reinforcement Learning—0
Neural Distillation as a State Representation Bottleneck in Reinforcement Learning—0
Neural Dynamic Policies for End-to-End Sensorimotor Learning—0
Neural Fictitious Self-Play on ELF Mini-RTS—0
Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265—0
Neural Heterogeneous Scheduler—0
Neural-Kernel Conditional Mean Embeddings—0
Neurally Plausible Reinforcement Learning of Working Memory Tasks—0
Neural Machine Translation with Adequacy-Oriented Learning—0
NEURAL MALWARE CONTROL WITH DEEP REINFORCEMENT LEARNING—0
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Benchmark Results

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