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 8126–8150 of 15113 papers

TitleStatusHype
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
Neural Math Word Problem Solver with Reinforcement Learning—0
Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning—0
Neural Network Approximation for Pessimistic Offline Reinforcement Learning—0
Neural Network Based Model Predictive Control for an Autonomous Vehicle—0
Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction—0
Neural Network Compatible Off-Policy Natural Actor-Critic Algorithm—0
Neural Network Compression for Reinforcement Learning Tasks—0
Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation—0
Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations—0
Neural Network Pruning Through Constrained Reinforcement Learning—0
Neural networks with motivation—0
Neural Network Verification in Control—0
Neural NID Rules—0
Neural Optimizer Search using Reinforcement Learning—0
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Benchmark Results

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