46 citations · 64 across the 7 of their papers we have counts for
8 papers
An Empirical Study of Deep Reinforcement Learning in Continuing Tasks
Yi Wan, Dmytro Korenkevych, Zheqing Zhu
In reinforcement learning (RL), continuing tasks refer to tasks where the agent-environment interaction is ongoing and can not be broken down into episodes. These tasks are suitabl…
Pearl: A Production-ready Reinforcement Learning Agent
Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari +12
Reinforcement learning (RL) is a versatile framework for optimizing long-term goals. Although many real-world problems can be formalized with RL, learning and deploying a performan…
Offline Reinforcement Learning for Optimizing Production Bidding Policies
Dmytro Korenkevych, Frank Cheng, Artsiom Balakir +5
The online advertising market, with its thousands of auctions run per second, presents a daunting challenge for advertisers who wish to optimize their spend under a budget constrai…
Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning
Ruiyang Xu, Jalaj Bhandari, Dmytro Korenkevych +4
Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected retur…
Autoregressive Policies for Continuous Control Deep Reinforcement Learning
Dmytro Korenkevych, A. Rupam Mahmood, Gautham Vasan +1
Reinforcement learning algorithms rely on exploration to discover new behaviors, which is typically achieved by following a stochastic policy. In continuous control tasks, policies…
Benchmarking Reinforcement Learning Algorithms on Real-World Robots
A. Rupam Mahmood, Dmytro Korenkevych, Gautham Vasan +2
Through many recent successes in simulation, model-free reinforcement learning has emerged as a promising approach to solving continuous control robotic tasks. The research communi…
Setting up a Reinforcement Learning Task with a Real-World Robot
A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer +1
Reinforcement learning is a promising approach to developing hard-to-engineer adaptive solutions for complex and diverse robotic tasks. However, learning with real-world robots is…
Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines
Dmytro Korenkevych, Yanbo Xue, Zhengbing Bian +4
Quantum annealing (QA) is a hardware-based heuristic optimization and sampling method applicable to discrete undirected graphical models. While similar to simulated annealing, QA r…