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 3711–3720 of 15113 papers

TitleStatusHype
Reinforcement Learning: Tutorial and Survey—0
Sparsity-based Safety Conservatism for Constrained Offline Reinforcement Learning—0
A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility—0
Balancing the Scales: Reinforcement Learning for Fair ClassificationCode0
GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control AgentsCode0
SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation—0
Deflated Dynamics Value Iteration—0
Learning to Steer Markovian Agents under Model UncertaintyCode0
Affordance-Guided Reinforcement Learning via Visual Prompting—0
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods—0
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Benchmark Results

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