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 8826–8850 of 15113 papers

TitleStatusHype
MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles—0
Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number—0
Reinforcement Learning for Markovian Bandits: Is Posterior Sampling more Scalable than Optimism?—0
Unbiased Methods for Multi-Goal Reinforcement Learning—0
Mungojerrie: Reinforcement Learning of Linear-Time Objectives—0
Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and DefensesCode0
Robust Reinforcement Learning Under Minimax Regret for Green SecurityCode0
Towards Safe Control of Continuum Manipulator Using Shielded Multiagent Reinforcement Learning—0
Residual Reinforcement Learning from Demonstrations—0
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity—0
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning—0
Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning—0
On the Power of Multitask Representation Learning in Linear MDP—0
Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning—0
Learning of feature points without additional supervision improves reinforcement learning from imagesCode0
Fundamental Limits of Reinforcement Learning in Environment with Endogeneous and Exogeneous Uncertainty—0
Deep reinforcement learning on a multi-asset environment for trading—0
Learning-Aided Heuristics Design for Storage System—0
Automatic Document Sketching: Generating Drafts from Analogous Texts—0
Targeted Data Acquisition for Evolving Negotiation Agents—0
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion—0
Which Mutual-Information Representation Learning Objectives are Sufficient for Control?—0
Online Sub-Sampling for Reinforcement Learning with General Function Approximation—0
Training like Playing: A Reinforcement Learning And Knowledge Graph-based framework for building Automatic Consultation System in Medical Field—0
User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning—0
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Benchmark Results

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