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 10726–10750 of 15113 papers

TitleStatusHype
Graph-based State Representation for Deep Reinforcement LearningCode0
Improving Sample Efficiency and Multi-Agent Communication in RL-based Train Rescheduling—0
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from PixelsCode1
The Immersion of Directed Multi-graphs in Embedding Fields. Generalisations—0
Transferable Active Grasping and Real Embodied DatasetCode1
Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?—0
First return, then exploreCode1
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement LearningCode1
Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP ProblemsCode0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
Age-Aware Status Update Control for Energy Harvesting IoT Sensors via Reinforcement Learning—0
The Ingredients of Real-World Robotic Reinforcement Learning—0
Reinforcement Learning Generalization with Surprise MinimizationCode0
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement LearningCode1
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning—0
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning ApproachCode1
CFR-RL: Traffic Engineering with Reinforcement Learning in SDNCode1
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information BudgetCode2
PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning—0
Self-Paced Deep Reinforcement LearningCode1
Automatic low-bit hybrid quantization of neural networks through meta learning—0
Learning Dialog Policies from Weak Demonstrations—0
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning—0
Correct Me If You Can: Learning from Error Corrections and MarkingsCode0
Guiding Robot Exploration in Reinforcement Learning via Automated Planning—0
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Benchmark Results

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