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 10126–10150 of 15113 papers

TitleStatusHype
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Data-efficient visuomotor policy training using reinforcement learning and generative models—0
Automated Discovery of Local Rules for Desired Collective-Level Behavior Through Reinforcement LearningCode0
Automated Database Indexing using Model-free Reinforcement Learning—0
Weak Human Preference Supervision For Deep Reinforcement LearningCode0
Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient—0
Safe Model-Based Reinforcement Learning for Systems with Parametric Uncertainties—0
Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles—0
Deep Inverse Reinforcement Learning for Structural Evolution of Small MoleculesCode0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
BabyAI 1.1Code1
Maximum Mutation Reinforcement Learning for Scalable ControlCode1
Bayesian Robust Optimization for Imitation LearningCode0
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued PoliciesCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning—0
Distributional Reinforcement Learning via Moment MatchingCode1
Value-Decomposition Multi-Agent Actor-CriticsCode1
Monte-Carlo Tree Search as Regularized Policy OptimizationCode1
Bridging the Imitation Gap by Adaptive Insubordination—0
Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems—0
Challenging common bolus advisor for self-monitoring type-I diabetes patients using Reinforcement LearningCode0
Integrating Deep Reinforcement Learning Networks with Health System SimulationsCode1
Adaptive Traffic Control with Deep Reinforcement Learning:Towards State-of-the-art and BeyondCode0
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning—0
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Benchmark Results

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