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 9001–9025 of 15113 papers

TitleStatusHype
Stochastic Approximation of Gaussian Free Energy for Risk-Sensitive Reinforcement Learning—0
Reinforcement learning for instance segmentation with high-level priors—0
Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games—0
Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using Reinforcement Learning—0
Revisiting Design Choices in Offline Model Based Reinforcement Learning—0
On Instrumental Variable Regression for Deep Offline Policy EvaluationCode0
RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search—0
On the use of feature-maps and parameter control for improved quality-diversity meta-evolution—0
Meta Reinforcement Learning for Fast Adaptation of Hierarchical Policies—0
Reinforcement Learning based Disease Progression Model for Alzheimer’s Disease—0
Objective-aware Traffic Simulation via Inverse Reinforcement Learning—0
Towards a Sample Efficient Reinforcement Learning Pipeline for Vision Based Robotics—0
Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning—0
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning—0
Deep Reinforcement Learning for Optimal Stopping with Application in Financial EngineeringCode0
Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images—0
Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning—0
Online Adaptive Optimal Control Algorithm Based on Synchronous Integral Reinforcement Learning With Explorations—0
Reinforcement Learning Assisted Oxygen Therapy for COVID-19 Patients Under Intensive Care—0
Online Multimodal Transportation Planning using Deep Reinforcement Learning—0
Reinforcement Learning for Adaptive Video Compressive Sensing—0
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization—0
Meta-Reinforcement Learning by Tracking Task Non-stationarityCode0
Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing—0
PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement Learning Policies—0
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Benchmark Results

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