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 3851–3860 of 15113 papers

TitleStatusHype
Model-free Quantum Gate Design and Calibration using Deep Reinforcement LearningCode0
An Online Model-Following Projection Mechanism Using Reinforcement Learning—0
Open Problems and Modern Solutions for Deep Reinforcement Learning—0
Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems—0
Generalization of Deep Reinforcement Learning for Jammer-Resilient Frequency and Power Allocation—0
Reinforcement Learning with History-Dependent Dynamic Contexts—0
Online Reinforcement Learning in Non-Stationary Context-Driven EnvironmentsCode0
Developing Driving Strategies Efficiently: A Skill-Based Hierarchical Reinforcement Learning Approach—0
Reinforcement Learning in Low-Rank MDPs with Density Features—0
Mind the Gap: Offline Policy Optimization for Imperfect RewardsCode1
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Benchmark Results

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