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 7951–8000 of 15113 papers

TitleStatusHype
Many Agent Reinforcement Learning Under Partial Observability—0
Modelling resource allocation in uncertain system environment through deep reinforcement learning—0
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual PoliciesCode1
Learning from Demonstration without DemonstrationsCode0
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
Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks—0
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL—0
Automatic Curricula via Expert Demonstrations—0
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
Safe Reinforcement Learning Using Advantage-Based InterventionCode1
MPC-based Reinforcement Learning for a Simplified Freight Mission of Autonomous Surface Vehicles—0
Unbiased Methods for Multi-Goal Reinforcement Learning—0
Revisiting the Weaknesses of Reinforcement Learning for Neural Machine TranslationCode1
Mungojerrie: Reinforcement Learning of Linear-Time Objectives—0
Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image TranslationCode1
Solving Continuous Control with Episodic MemoryCode1
Real-time Adversarial Perturbations against Deep Reinforcement Learning Policies: Attacks and DefensesCode0
Reinforcement Learning for Markovian Bandits: Is Posterior Sampling more Scalable than Optimism?—0
Reinforcement learning for pursuit and evasion of microswimmers at low Reynolds number—0
Offline RL Without Off-Policy EvaluationCode1
rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot SoccerCode1
Robust Reinforcement Learning Under Minimax Regret for Green SecurityCode0
Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning—0
Deep reinforcement learning on a multi-asset environment for trading—0
Fundamental Limits of Reinforcement Learning in Environment with Endogeneous and Exogeneous Uncertainty—0
Deep Reinforcement Learning for Conservation DecisionsCode1
Learning of feature points without additional supervision improves reinforcement learning from imagesCode0
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity—0
Residual Reinforcement Learning from Demonstrations—0
Randomized Exploration for Reinforcement Learning with General Value Function ApproximationCode1
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning—0
Towards Safe Control of Continuum Manipulator Using Shielded Multiagent Reinforcement Learning—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
Targeted Data Acquisition for Evolving Negotiation Agents—0
Efficient (Soft) Q-Learning for Text Generation with Limited Good DataCode1
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers—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
Automatic Document Sketching: Generating Drafts from Analogous Texts—0
Learning-Aided Heuristics Design for Storage System—0
Learning Intrusion Prevention Policies through Optimal StoppingCode1
Online Sub-Sampling for Reinforcement Learning with General Function Approximation—0
Which Mutual-Information Representation Learning Objectives are Sufficient for Control?—0
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion—0
Representation Learning for Out-of-distribution Generalization in Reinforcement Learning—0
Reinforcement Learning as One Big Sequence Modeling ProblemCode1
MASAI: Multi-agent Summative Assessment Improvement for Unsupervised Environment DesignCode0
Tangent Space Least Adaptive Clustering—0
Show:102550
← PrevPage 160 of 303Next →

Benchmark Results

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