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 7826–7850 of 15113 papers

TitleStatusHype
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity—0
Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes—0
Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization—0
Hierarchical Expert Networks for Meta-Learning—0
Hierarchical Imitation and Reinforcement Learning—0
Developing Driving Strategies Efficiently: A Skill-Based Hierarchical Reinforcement Learning Approach—0
Hierarchical Linearly-Solvable Markov Decision Problems—0
Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation—0
Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment—0
Hierarchical Modular Reinforcement Learning Method and Knowledge Acquisition of State-Action Rule for Multi-target Problem—0
Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters—0
Hierarchical Policy Learning is Sensitive to Goal Space Design—0
Hierarchical Policy Search via Return-Weighted Density Estimation—0
On Oracle-Efficient PAC RL with Rich Observations—0
On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making—0
On overfitting and asymptotic bias in batch reinforcement learning with partial observability—0
On-policy Actor-Critic Reinforcement Learning for Multi-UAV Exploration—0
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion—0
On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation—0
On-Policy Model Errors in Reinforcement Learning—0
On-Policy Optimization with Group Equivalent Preference for Multi-Programming Language Understanding—0
On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling—0
Policy Optimization Reinforcement Learning with Entropy Regularization—0
On-Policy Robot Imitation Learning from a Converging Supervisor—0
On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm—0
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Benchmark Results

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