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 11201–11225 of 15113 papers

TitleStatusHype
Learning to Fly via Deep Model-Based Reinforcement LearningCode0
Adjust Planning Strategies to Accommodate Reinforcement Learning AgentsCode0
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles—0
Viewport-Aware Deep Reinforcement Learning Approach for 360^o Video Caching—0
Placement Optimization with Deep Reinforcement Learning—0
Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systems—0
A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation AssuranceCode0
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks—0
Reinforcement Learning for Electricity Network Operation—0
Provably Efficient Exploration for Reinforcement Learning Using Unsupervised LearningCode0
Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous—0
A General Framework for Learning Mean-Field Games—0
Taylor Expansion Policy Optimization—0
Optimizing Medical Treatment for Sepsis in Intensive Care: from Reinforcement Learning to Pre-Trial Evaluation—0
Option Discovery in the Absence of Rewards with Manifold AnalysisCode0
The Chef's Hat Simulation Environment for Reinforcement-Learning-Based AgentsCode0
Analyzing Visual Representations in Embodied Navigation Tasks—0
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning—0
Exploring Unknown States with Action BalanceCode0
Automatic Curriculum Learning For Deep RL: A Short Survey—0
Explore and Exploit with Heterotic Line Bundle ModelsCode0
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey—0
Mobility Management for Cellular-Connected UAVs: A Learning-Based Approach—0
SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks—0
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning—0
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Benchmark Results

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