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

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Showing 11751–11775 of 15113 papers

TitleStatusHype
Gamifying the Vehicle Routing Problem with Stochastic Requests—0
Deep Reinforcement Learning for Adaptive Traffic Signal Control—0
A Reduction from Reinforcement Learning to No-Regret Online Learning—0
Reinforcement Learning for Market Making in a Multi-agent Dealer MarketCode0
Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specifications—0
A Convergent Off-Policy Temporal Difference AlgorithmCode0
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning—0
Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning—0
Learning to Communicate in Multi-Agent Reinforcement Learning : A Review—0
Asymptotics of Reinforcement Learning with Neural Networks—0
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning—0
Accelerating Training in Pommerman with Imitation and Reinforcement Learning—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
Real-Time Reinforcement LearningCode0
Multi-Agent Connected Autonomous Driving using Deep Reinforcement LearningCode0
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems—0
SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement LearningCode0
DRiLLS: Deep Reinforcement Learning for Logic SynthesisCode0
Driving Reinforcement Learning with ModelsCode0
Learning to Order Sub-questions for Complex Question Answering—0
Context-aware Active Multi-Step Reinforcement Learning—0
Model-Based Reinforcement Learning with Adversarial Training for Online RecommendationCode0
Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing—0
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Benchmark Results

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