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 11551–11600 of 15113 papers

TitleStatusHype
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation TasksCode0
Hebbian Synaptic Modifications in Spiking Neurons that Learn—0
Inverse Reinforcement Learning with Missing Data—0
Generalized Maximum Causal Entropy for Inverse Reinforcement Learning—0
Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift—0
Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in HealthcareCode0
Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance—0
Empirical Study of Off-Policy Policy Evaluation for Reinforcement LearningCode0
Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient—0
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning—0
Deep Reinforcement Learning for Adaptive Traffic Signal Control—0
A Reduction from Reinforcement Learning to No-Regret Online Learning—0
Gamifying the Vehicle Routing Problem with Stochastic Requests—0
Reinforcement Learning for Market Making in a Multi-agent Dealer MarketCode0
Asymptotics of Reinforcement Learning with Neural Networks—0
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning—0
A Convergent Off-Policy Temporal Difference AlgorithmCode0
Learning to Communicate in Multi-Agent Reinforcement Learning : A Review—0
Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning—0
Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specifications—0
One-shot learning and behavioral eligibility traces in sequential decision making—0
Schedule Earth Observation satellites with Deep Reinforcement Learning—0
MSDF: A Deep Reinforcement Learning Framework for Service Function Chain Migration—0
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning—0
Accelerating Training in Pommerman with Imitation and Reinforcement Learning—0
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems—0
SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement LearningCode0
Multi-Agent Connected Autonomous Driving using Deep Reinforcement LearningCode0
Real-Time Reinforcement LearningCode0
Driving Reinforcement Learning with ModelsCode0
Context-aware Active Multi-Step Reinforcement Learning—0
DRiLLS: Deep Reinforcement Learning for Logic SynthesisCode0
Learning to Order Sub-questions for Complex Question Answering—0
Value-Added Chemical Discovery Using Reinforcement Learning—0
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy—0
Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing—0
Model-Based Reinforcement Learning with Adversarial Training for Online RecommendationCode0
Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning—0
Learning to reinforcement learn for Neural Architecture SearchCode0
Worst Cases Policy Gradients—0
Contrastive Multi-document Question GenerationCode0
Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning—0
H_ Model-free Reinforcement Learning with Robust Stability GuaranteeCode0
Option Compatible Reward Inverse Reinforcement Learning—0
Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports—0
MBCAL: Sample Efficient and Variance Reduced Reinforcement Learning for Recommender Systems—0
Experience Sharing Between Cooperative Reinforcement Learning Agents—0
Distributional Reward Decomposition for Reinforcement Learning—0
Improving reinforcement learning algorithms: towards optimal learning rate policiesCode0
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Benchmark Results

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