400 citations · 506 across the 9 of their papers we have counts for
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cs.AI2020
Griddly: A platform for AI research in games
Chris Bamford, Shengyi Huang, Simon Lucas
In recent years, there have been immense breakthroughs in Game AI research, particularly with Reinforcement Learning (RL). Despite their success, the underlying games are usually i…
cs.LG2020★ 5 cited
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games
Shengyi Huang, Santiago Ontañón
Training agents using Reinforcement Learning in games with sparse rewards is a challenging problem, since large amounts of exploration are required to retrieve even the first rewar…
cs.LG2020★ 400 cited
A Closer Look at Invalid Action Masking in Policy Gradient Algorithms
Shengyi Huang, Santiago Ontañón
In recent years, Deep Reinforcement Learning (DRL) algorithms have achieved state-of-the-art performance in many challenging strategy games. Because these games have complicated ru…