no code implementations • 10 Oct 2023 • Jacob Chmura, Hasham Burhani, Xiao Qi Shi
We expand on this topic and propose a new intrinsic reward that systemically quantifies exploratory behavior and promotes state coverage by maximizing the information content of a trajectory taken by an agent.
no code implementations • 8 Aug 2023 • Hasham Burhani, Xiao Qi Shi, Jonathan Jaegerman, Daniel Balicki
From our analysis of the aforementioned problems we derive a novel loss function for reinforcement learning and supervised classification.
no code implementations • 16 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
1 code implementation • 16 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.
2 code implementations • 24 Jul 2020 • Karush Suri, Xiao Qi Shi, Konstantinos N. Plataniotis, Yuri A. Lawryshyn
Advances in Reinforcement Learning (RL) have demonstrated data efficiency and optimal control over large state spaces at the cost of scalable performance.