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 8801–8825 of 15113 papers

TitleStatusHype
Scientific multi-agent reinforcement learning for wall-models of turbulent flows—0
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net—0
Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach—0
Boosting Offline Reinforcement Learning with Residual Generative Modeling—0
Goal-Directed Planning by Reinforcement Learning and Active Inference—0
Adversarially Trained Neural Policies in the Fourier Domain—0
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result—0
The Curse of Passive Data Collection in Batch Reinforcement Learning—0
Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents—0
Sample Efficient Social Navigation Using Inverse Reinforcement Learning—0
Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments—0
Non-Robust Feature Mapping in Deep Reinforcement Learning—0
Proper Value EquivalenceCode0
Many Agent Reinforcement Learning Under Partial Observability—0
Modelling resource allocation in uncertain system environment through deep reinforcement learning—0
Learning from Demonstration without DemonstrationsCode0
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes—0
Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks—0
A Reinforcement Learning Approach for an IRS-assisted NOMA Network—0
Adapting the Function Approximation Architecture in Online Reinforcement Learning—0
CROP: Certifying Robust Policies for Reinforcement Learning through Functional SmoothingCode0
A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents—0
Deep Reinforcement Learning Based Optimization for IRS Based UAV-NOMA Downlink Networks—0
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL—0
Automatic Curricula via Expert Demonstrations—0
Show:102550
← PrevPage 353 of 605Next →

Benchmark Results

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