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 11551–11575 of 15113 papers

TitleStatusHype
Mastering Complex Control in MOBA Games with Deep Reinforcement Learning—0
Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning—0
Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data—0
Deep Reinforcement Learning for Motion Planning of Mobile Robots—0
Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse using Root-Flipping: DeepRF_SLR—0
Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization ApproachCode0
Deep Reinforcement Learning for Smart Home Energy Management—0
Extendable NFV-Integrated Control Method Using Reinforcement Learning—0
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and LimitationsCode0
Learning to grow: control of material self-assembly using evolutionary reinforcement learning—0
Distributional Reinforcement Learning for Energy-Based Sequential ModelsCode0
Benchmarking the Neural Linear Model for Regression—0
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation—0
Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning—0
Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software—0
MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement LearningCode0
Coordination in Adversarial Sequential Team Games via Multi-Agent Deep Reinforcement Learning—0
KARL: Knowledge-Aware Reasoning Memory Modeling with Reinforcement Learning of Vector Space—0
Planning with Abstract Learned Models While Learning Transferable Subtasks—0
UNAS: Differentiable Architecture Search Meets Reinforcement LearningCode0
PixelRL: Fully Convolutional Network with Reinforcement Learning for Image ProcessingCode0
Spatial Influence-aware Reinforcement Learning for Intelligent Transportation System—0
Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator—0
Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods—0
Bayesian Linear Regression on Deep Representations—0
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Benchmark Results

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