6 papers
Playing Atari Space Invaders with Sparse Cosine Optimized Policy Evolution
Jim O'Connor, Jay B. Nash, Derin Gezgin +1
Evolutionary approaches have previously been shown to be effective learning methods for a diverse set of domains. However, the domain of game-playing poses a particular challenge f…
Evolutionary Optimization of Deep Learning Agents for Sparrow Mahjong
Jim O'Connor, Derin Gezgin, Gary B. Parker
We present Evo-Sparrow, a deep learning-based agent for AI decision-making in Sparrow Mahjong, trained by optimizing Long Short-Term Memory (LSTM) networks using Covariance Matrix…
Evolving Neural Controllers for Xpilot-AI Racing Using Neuroevolution of Augmenting Topologies
Jim O'Connor, Nicholas Lorentzen, Gary B. Parker +1
This paper investigates the development of high-performance racing controllers for a newly implemented racing mode within the Xpilot-AI platform, utilizing the Neuro Evolution of A…
SCOPE for Hexapod Gait Generation
Jim O'Connor, Jay B. Nash, Derin Gezgin +1
Evolutionary methods have previously been shown to be an effective learning method for walking gaits on hexapod robots. However, the ability of these algorithms to evolve an effect…
Learning Dark Souls Combat Through Pixel Input With Neuroevolution
Jim O'Connor, Gary B. Parker, Mustafa Bugti
This paper investigates the application of Neuroevolution of Augmenting Topologies (NEAT) to automate gameplay in Dark Souls, a notoriously challenging action role-playing game cha…
NeuroPAL: Punctuated Anytime Learning with Neuroevolution for Macromanagement in Starcraft: Brood War
Jim O'Connor, Yeonghun Lee, Gary B Parker
StarCraft: Brood War remains a challenging benchmark for artificial intelligence research, particularly in the domain of macromanagement, where long-term strategic planning is requ…