Search Results for author: Yuri Lawryshyn

Found 3 papers, 1 papers with code

TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution

no code implementations16 Feb 2021 Karush Suri, Xiao Qi Shi, Konstantinos Plataniotis, Yuri Lawryshyn

We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem.

Hierarchical Reinforcement Learning reinforcement-learning +1

Energy-based Surprise Minimization for Multi-Agent Value Factorization

1 code implementation16 Sep 2020 Karush Suri, Xiao Qi Shi, Konstantinos Plataniotis, Yuri Lawryshyn

(2) EMIX highlights a practical use of energy functions in MARL with theoretical guarantees and experiment validations of the energy operator.

Multi-agent Reinforcement Learning Q-Learning +2

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