22 citations · 42 across the 7 of their papers we have counts for
4 papers · 1 filter
Recurrent Off-policy Baselines for Memory-based Continuous Control
Zhihan Yang, Hai Nguyen
When the environment is partially observable (PO), a deep reinforcement learning (RL) agent must learn a suitable temporal representation of the entire history in addition to a str…
Conditional Level Generation and Game Blending
Anurag Sarkar, Zhihan Yang, Seth Cooper
Prior research has shown variational autoencoders (VAEs) to be useful for generating and blending game levels by learning latent representations of existing level data. We build on…
Game Level Clustering and Generation using Gaussian Mixture VAEs
Zhihan Yang, Anurag Sarkar, Seth Cooper
Variational autoencoders (VAEs) have been shown to be able to generate game levels but require manual exploration of the learned latent space to generate outputs with desired attri…
Controllable Level Blending between Games using Variational Autoencoders
Anurag Sarkar, Zhihan Yang, Seth Cooper
Previous work explored blending levels from existing games to create levels for a new game that mixes properties of the original games. In this paper, we use Variational Autoencode…