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 6751–6775 of 15113 papers

TitleStatusHype
Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning—0
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning—0
Dyna Planning using a Feature Based Generative Model—0
Dyna-T: Dyna-Q and Upper Confidence Bounds Applied to Trees—0
DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning—0
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization—0
EasyRL: A Simple and Extensible Reinforcement Learning Framework—0
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning—0
Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach—0
EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints—0
Ecological Reinforcement Learning—0
ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning—0
Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning—0
e-COP : Episodic Constrained Optimization of Policies—0
Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach—0
Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms—0
Edge AI-Powered Real-Time Decision-Making for Autonomous Vehicles in Adverse Weather Conditions—0
Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning—0
Edge-Compatible Reinforcement Learning for Recommendations—0
EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge—0
EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning—0
EEG_RL-Net: Enhancing EEG MI Classification through Reinforcement Learning-Optimised Graph Neural Networks—0
Generalization through Diversity: Improving Unsupervised Environment Design—0
Effective Exploration for Deep Reinforcement Learning via Bootstrapped Q-Ensembles under Tsallis Entropy Regularization—0
Effective Medical Test Suggestions Using Deep Reinforcement Learning—0
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Benchmark Results

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