6 citations · 15 across the 17 of their papers we have counts for
5 papers · 1 filter
Best Agent Identification for General Game Playing
Matthew Stephenson, Alex Newcombe, Eric Piette +1
We present an efficient and generalised procedure to accurately identify the best (or near best) performing algorithm for each sub-task in a multi-problem domain. Our approach trea…
Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds
Frederic Abraham, Matthew Stephenson
This paper investigates the suitability of using Generative Adversarial Networks (GANs) to generate stable structures for the physics-based puzzle game Angry Birds. While previous…
Manipulating the Distributions of Experience used for Self-Play Learning in Expert Iteration
Dennis J. N. J. Soemers, Éric Piette, Matthew Stephenson +1
Expert Iteration (ExIt) is an effective framework for learning game-playing policies from self-play. ExIt involves training a policy to mimic the search behaviour of a tree search…
Superstition in the Network: Deep Reinforcement Learning Plays Deceptive Games
Philip Bontrager, Ahmed Khalifa, Damien Anderson +3
Deep reinforcement learning has learned to play many games well, but failed on others. To better characterize the modes and reasons of failure of deep reinforcement learners, we te…
Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates
Dennis J. N. J. Soemers, Éric Piette, Matthew Stephenson +1
In recent years, state-of-the-art game-playing agents often involve policies that are trained in self-playing processes where Monte Carlo tree search (MCTS) algorithms and trained…