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 3141–3150 of 15113 papers

TitleStatusHype
IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of InterestingnessCode0
REX: Rapid Exploration and eXploitation for AI Agents—0
Continuous-Time Reinforcement Learning: New Design Algorithms with Theoretical Insights and Performance Guarantees—0
Quarl: A Learning-Based Quantum Circuit Optimizer—0
Natural Actor-Critic for Robust Reinforcement Learning with Function ApproximationCode1
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient—0
Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology—0
Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning—0
Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning—0
POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenanceCode0
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Benchmark Results

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