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 5526–5550 of 15113 papers

TitleStatusHype
An Analysis of Quantile Temporal-Difference Learning—0
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models—0
Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning—0
SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning—0
Towards AI-controlled FES-restoration of arm movements: Controlling for progressive muscular fatigue with Gaussian state-space models—0
Towards AI-controlled FES-restoration of arm movements: neuromechanics-based reinforcement learning for 3-D reaching—0
Why People Skip Music? On Predicting Music Skips using Deep Reinforcement LearningCode0
Hint assisted reinforcement learning: an application in radio astronomyCode0
Actor-Director-Critic: A Novel Deep Reinforcement Learning Framework—0
Learning to Perceive in Deep Model-Free Reinforcement LearningCode0
Exploration in Model-based Reinforcement Learning with Randomized Reward—0
Minimax Weight Learning for Absorbing MDPs—0
Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning—0
Tuning Path Tracking Controllers for Autonomous Cars Using Reinforcement Learning—0
A Survey on Transformers in Reinforcement Learning—0
Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior—0
Mathematical Models and Reinforcement Learning based Evolutionary Algorithm Framework for Satellite Scheduling Problem—0
Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN—0
Markov Chain Concentration with an Application in Reinforcement Learning—0
Provable Reset-free Reinforcement Learning by No-Regret Reduction—0
Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads—0
A Deep Reinforcement Learning-Based Controller for Magnetorheological-Damped Vehicle Suspension—0
Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Games—0
Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization—0
Reinforcement Learning-Based Air Traffic Deconfliction—0
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Benchmark Results

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