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 8926–8950 of 15113 papers

TitleStatusHype
Reinforced Video Captioning with Entailment Rewards—0
Reinforced Workload Distribution Fairness—0
Reinforcement and Imitation Learning via Interactive No-Regret Learning—0
Reinforcement-based frugal learning for satellite image change detection—0
Reinforcement Evolutionary Learning Method for self-learning—0
Reinforcement Explanation Learning—0
Reinforcement Leaning for Infinite-Dimensional Systems—0
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound—0
Reinforcement Learning: a Comparison of UCB Versus Alternative Adaptive Policies—0
Reinforcement Learning Agent Design and Optimization with Bandwidth Allocation Model—0
Reinforcement Learning Agents for Ubisoft's Roller Champions—0
Reinforcement Learning Agent Training with Goals for Real World Tasks—0
Reinforcement Learning Algorithm for Traffic Steering in Heterogeneous Network—0
Reinforcement Learning Algorithms: An Overview and Classification—0
Reinforcement Learning Algorithm Selection—0
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
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Benchmark Results

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