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 3561–3570 of 15113 papers

TitleStatusHype
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management—0
Hybrid Systems Neural Control with Region-of-Attraction Planner—0
Comparing NARS and Reinforcement Learning: An Analysis of ONA and Q-Learning Algorithms—0
Measurement Optimization under Uncertainty using Deep Reinforcement Learning—0
Dynamic Update-to-Data Ratio: Minimizing World Model OverfittingCode0
Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Control—0
A Data-Driven Model-Reference Adaptive Control Approach Based on Reinforcement Learning—0
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs—0
A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games—0
Online Reinforcement Learning in Periodic MDP—0
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Benchmark Results

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