143 citations · 673 across the 47 of their papers we have counts for
5 papers · 1 filter
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager +3
Deep reinforcement learning (RL) has shown impressive results in a variety of domains, learning directly from high-dimensional sensory streams. However, when neural networks are tr…
Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network
Vanessa Volz, Jacob Schrum, Jialin Liu +3
Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content…
A Robot to Shape your Natural Plant: The Machine Learning Approach to Model and Control Bio-Hybrid Systems
Mostafa Wahby, Mary Katherine Heinrich, Daniel Nicolas Hofstadler +5
Bio-hybrid systems---close couplings of natural organisms with technology---are high potential and still underexplored. In existing work, robots have mostly influenced group behavi…
Automated Curriculum Learning by Rewarding Temporally Rare Events
Niels Justesen, Sebastian Risi
Reward shaping allows reinforcement learning (RL) agents to accelerate learning by receiving additional reward signals. However, these signals can be difficult to design manually,…
Deep Interactive Evolution
Philip Bontrager, Wending Lin, Julian Togelius +1
This paper describes an approach that combines generative adversarial networks (GANs) with interactive evolutionary computation (IEC). While GANs can be trained to produce lifelike…