187 citations · 331 across the 16 of their papers we have counts for
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cs.LG2016★ 7 cited
Separation of Concerns in Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Joshua Romoff +1
In this paper, we propose a framework for solving a single-agent task by using multiple agents, each focusing on different aspects of the task. This approach has two main advantage…
cs.AI2016★ 13 cited
Effective Multi-step Temporal-Difference Learning for Non-Linear Function Approximation
Harm van Seijen
Multi-step temporal-difference (TD) learning, where the update targets contain information from multiple time steps ahead, is one of the most popular forms of TD learning for linea…