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 9976–10000 of 15113 papers

TitleStatusHype
Constrained Markov Decision Processes via Backward Value Functions—0
Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon—0
Robust Reinforcement Learning: A Case Study in Linear Quadratic Regulation—0
t-Soft Update of Target Network for Deep Reinforcement Learning—0
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing—0
Ensuring Monotonic Policy Improvement in Entropy-regularized Value-based Reinforcement Learning—0
Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach—0
Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning—0
Improved Memories Learning—0
Learning Off-Policy with Online PlanningCode1
DSP: A Differential Spatial Prediction Scheme for Comprehensive real industrial datasets—0
Adaptive and Multiple Time-scale Eligibility Traces for Online Deep Reinforcement Learning—0
Mobile Networks for Computer Go—0
Social-Aware Incentive Mechanism for VehicularCrowdsensing by Deep Reinforcement LearningCode1
Model-Free Episodic Control with State Aggregation—0
Biomechanic Posture Stabilisation via Iterative Training of Multi-policy Deep Reinforcement Learning Agents—0
Adversarial Imitation Learning via Random Search—0
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication—0
A Composable Specification Language for Reinforcement Learning TasksCode1
Reinforcement Learning-based Admission Control in Delay-sensitive Service Systems—0
NANCY: Neural Adaptive Network Coding methodologY for video distribution over wireless networks—0
Static Neural Compiler Optimization via Deep Reinforcement Learning—0
Model-free optimal control of discrete-time systems with additive and multiplicative noises—0
Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting—0
Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learningCode1
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Benchmark Results

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