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 4421–4430 of 15113 papers

TitleStatusHype
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control—0
De novo PROTAC design using graph-based deep generative modelsCode1
Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AICode0
The Benefits of Model-Based Generalization in Reinforcement LearningCode0
Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for RoboticsCode1
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning—0
Synthesis of separation processes with reinforcement learningCode1
A Survey on Reinforcement Learning in Aviation Applications—0
Learning safety in model-based Reinforcement Learning using MPC and Gaussian ProcessesCode1
Contrastive Value Learning: Implicit Models for Simple Offline RL—0
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Benchmark Results

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