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 4561–4570 of 15113 papers

TitleStatusHype
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement LearningCode0
Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes—0
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design—0
Learning Preferences for Interactive AutonomyCode0
CLUTR: Curriculum Learning via Unsupervised Task Representation LearningCode0
Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification—0
DIAMBRA Arena: a New Reinforcement Learning Platform for Research and ExperimentationCode2
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with DemonstrationsCode0
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Benchmark Results

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