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 1051–1060 of 15113 papers

TitleStatusHype
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement LearningCode1
Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement LearningCode1
Goal-Conditioned Generators of Deep PoliciesCode1
Stabilizing Off-Policy Deep Reinforcement Learning from PixelsCode1
Modular Lifelong Reinforcement Learning via Neural CompositionCode1
Denoised MDPs: Learning World Models Better Than the World ItselfCode1
On the Learning and Learnability of QuasimetricsCode1
Short-Term Plasticity Neurons Learning to Learn and ForgetCode1
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement LearningCode1
Multi-Agent Car Parking using Reinforcement LearningCode1
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Benchmark Results

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