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 7126–7150 of 15113 papers

TitleStatusHype
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making—0
Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning—0
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning—0
Experience-driven Networking: A Deep Reinforcement Learning based Approach—0
Experience enrichment based task independent reward model—0
Experience Replay More When It's a Key Transition in Deep Reinforcement Learning—0
Experience Replay Optimization—0
Experience Replay Using Transition Sequences—0
Experience Sharing Between Cooperative Reinforcement Learning Agents—0
Experimental analysis of data-driven control for a building heating system—0
Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar—0
Experimental Evidence that Empowerment May Drive Exploration in Sparse-Reward Environments—0
Experimental results : Reinforcement Learning of POMDPs using Spectral Methods—0
Experimental Study on Reinforcement Learning-based Control of an Acrobot—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning—0
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples—0
Explainability in Deep Reinforcement Learning—0
Explainability in Deep Reinforcement Learning, a Review into Current Methods and Applications—0
Explainability in reinforcement learning: perspective and position—0
Explainable AI: Deep Reinforcement Learning Agents for Residential Demand Side Cost Savings in Smart Grids—0
Explainable AI for UAV Mobility Management: A Deep Q-Network Approach for Handover Minimization—0
Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems—0
Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs—0
Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach—0
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Benchmark Results

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