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 4676–4700 of 15113 papers

TitleStatusHype
Reinforcement Learning algorithms for regret minimization in structured Markov Decision Processes—0
Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook—0
Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology—0
Towards interpretable quantum machine learning via single-photon quantum walks—0
Reinforcement Learning and Deep Stochastic Optimal Control for Final Quadratic Hedging—0
Reinforcement Learning and Graph Neural Networks for Probabilistic Risk Assessment—0
Reinforcement Learning and Inverse Reinforcement Learning with System 1 and System 2—0
Reinforcement Learning and Mixed-Integer Programming for Power Plant Scheduling in Low Carbon Systems: Comparison and Hybridisation—0
Reinforcement Learning and Nonparametric Detection of Game-Theoretic Equilibrium Play in Social Networks—0
Reinforcement Learning and Video Games—0
Reinforcement Learning Applications—0
Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice—0
Reinforcement Learning applied to Single Neuron—0
Reinforcement Learning Applied to Trading Systems: A Survey—0
Reinforcement Learning Approaches for the Orienteering Problem with Stochastic and Dynamic Release Dates—0
Reinforcement Learning Approaches in Social Robotics—0
Reinforcement Learning Approach for Parallelization in Filters Aggregation Based Feature Selection Algorithms—0
Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs—0
Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems—0
Reinforcement Learning approach for Real Time Strategy Games Battle city and S3—0
Reinforcement learning approach for resource allocation in humanitarian logistics—0
Reinforcement Learning Approach to Active Learning for Image Classification—0
Reinforcement Learning Approach to Estimation in Linear Systems—0
Reinforcement Learning Approach to Optimizing Profilometric Sensor Trajectories for Surface Inspection—0
Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems—0
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Benchmark Results

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