Search Results for author: Dmytro Korenkevych

Found 7 papers, 4 papers with code

Offline Reinforcement Learning for Optimizing Production Bidding Policies

no code implementations13 Oct 2023 Dmytro Korenkevych, Frank Cheng, Artsiom Balakir, Alex Nikulkov, Lingnan Gao, Zhihao Cen, Zuobing Xu, Zheqing Zhu

We use a hybrid agent architecture that combines arbitrary base policies with deep neural networks, where only the optimized base policy parameters are eventually deployed, and the neural network part is discarded after training.


Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning

no code implementations23 May 2023 Ruiyang Xu, Jalaj Bhandari, Dmytro Korenkevych, Fan Liu, Yuchen He, Alex Nikulkov, Zheqing Zhu

Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected return metrics, neglecting the downstream effects of recommendations on user behavior.

Recommendation Systems reinforcement-learning

Autoregressive Policies for Continuous Control Deep Reinforcement Learning

1 code implementation27 Mar 2019 Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan, James Bergstra

We introduce a family of stationary autoregressive (AR) stochastic processes to facilitate exploration in continuous control domains.

Continuous Control reinforcement-learning +1

Benchmarking Reinforcement Learning Algorithms on Real-World Robots

2 code implementations20 Sep 2018 A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan, William Ma, James Bergstra

The research community is now able to reproduce, analyze and build quickly on these results due to open source implementations of learning algorithms and simulated benchmark tasks.

Benchmarking Continuous Control +2

Setting up a Reinforcement Learning Task with a Real-World Robot

2 code implementations19 Mar 2018 A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer, James Bergstra

Reinforcement learning is a promising approach to developing hard-to-engineer adaptive solutions for complex and diverse robotic tasks.

reinforcement-learning Reinforcement Learning (RL)

Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines

no code implementations14 Nov 2016 Dmytro Korenkevych, Yanbo Xue, Zhengbing Bian, Fabian Chudak, William G. Macready, Jason Rolfe, Evgeny Andriyash

We argue that this relates to the fact that we are training a quantum rather than classical Boltzmann distribution in this case.


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