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 4901–4925 of 15113 papers

TitleStatusHype
A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving—0
A Review of Uncertainty for Deep Reinforcement Learning—0
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges—0
Car-Following Models: A Multidisciplinary Review—0
Argumentative Reward Learning: Reasoning About Human Preferences—0
Argus: Smartphone-enabled Human Cooperation via Multi-Agent Reinforcement Learning for Disaster Situational Awareness—0
ARIA: Training Language Agents with Intention-Driven Reward Aggregation—0
A Policy Optimization Method Towards Optimal-time Stability—0
A Roadmap Towards Improving Multi-Agent Reinforcement Learning With Causal Discovery And Inference—0
A Robotic Model of Hippocampal Reverse Replay for Reinforcement Learning—0
A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems—0
A Robust Fuel Optimization Strategy For Hybrid Electric Vehicles: A Deep Reinforcement Learning Based Continuous Time Design Approach—0
ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch—0
Artificial Intelligence Approaches To UCAV Autonomy—0
Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo—0
Artificial Intelligence-based Decision Support Systems for Precision and Digital Health—0
Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3—0
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning—0
A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants—0
A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control—0
Safe Model-Based Reinforcement Learning for Systems with Parametric Uncertainties—0
A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement Learning and Application in UAV Hovering—0
A Scalable Deep Reinforcement Learning Model for Online Scheduling Coflows of Multi-Stage Jobs for High Performance Computing—0
A Scalable Finite Difference Method for Deep Reinforcement Learning—0
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions—0
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Benchmark Results

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