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Anton Plaksin

2 accepted papers

2024

Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach

ICML 2024poster

Robust Reinforcement Learning (RRL) is a promising Reinforcement Learning (RL) paradigm aimed at training robust to uncertainty or disturbances models, making them more efficient for real-world applications. Following this paradigm, uncertainty or disturbances are interpreted as actions of a second…

Cited by 1SourcePDFScholar
2022

Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis

NeurIPS 2022accept

One of the most effective continuous deep reinforcement learning algorithms is normalized advantage functions (NAF). The main idea of NAF consists in the approximation of the Q-function by functions quadratic with respect to the action variable. This idea allows to apply the algorithm to continuous…

Cited by 1SourcePDFScholar