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 4001–4025 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
Adversarial Online Multi-Task Reinforcement LearningCode0
Hint assisted reinforcement learning: an application in radio astronomyCode0
Learning to Perceive in Deep Model-Free Reinforcement LearningCode0
Actor-Director-Critic: A Novel Deep Reinforcement Learning Framework—0
Why People Skip Music? On Predicting Music Skips using Deep Reinforcement LearningCode0
Towards AI-controlled FES-restoration of arm movements: Controlling for progressive muscular fatigue with Gaussian state-space models—0
Orbit: A Unified Simulation Framework for Interactive Robot Learning EnvironmentsCode5
schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling ExperimentsCode1
Mastering Diverse Domains through World ModelsCode4
Towards AI-controlled FES-restoration of arm movements: neuromechanics-based reinforcement learning for 3-D reaching—0
Network Slicing via Transfer Learning aided Distributed Deep Reinforcement Learning—0
Tuning Path Tracking Controllers for Autonomous Cars Using Reinforcement Learning—0
Minimax Weight Learning for Absorbing MDPs—0
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Exploration in Model-based Reinforcement Learning with Randomized Reward—0
Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior—0
A Survey on Transformers in Reinforcement Learning—0
Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN—0
Mathematical Models and Reinforcement Learning based Evolutionary Algorithm Framework for Satellite Scheduling Problem—0
Markov Chain Concentration with an Application in Reinforcement Learning—0
Provable Reset-free Reinforcement Learning by No-Regret Reduction—0
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Benchmark Results

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