46 citations · 64 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…