Paul Templier

Paul Templier

PhD Student in Evolutionary Reinforcement Learning at ISAE SUPAERO

Publications

A Geometric Encoding for Neural Network Evolution

Authors: Paul Templier, Emmanuel Rachelson, Dennis G. Wilson

Published in GECCO 2021 (Genetic and Evolutionary Computation Conference), 2021

A major limitation to the optimization of artificial neural networks (ANN) with evolutionary methods lies in the high dimensionality of the search space, the number of weights growing quadratically with the size of the network. This leads to expensive training costs, especially in evolution strategies which rely on matrices whose sizes grow with the number of genes. We introduce a geometric encoding for neural network evolution (GENE) as a representation of ANN parameters in a smaller space that scales linearly with the number of neurons, allowing for efficient parameter search. Each neuron of the network is encoded as a point in a latent space and the weight of a connection between two neurons is computed as the distance between them. The coordinates of all neurons are then optimized with evolution strategies in a reduced search space while not limiting network fitness and possibly improving search.

Evolving a Dota 2 bot: Illuminating search in CGP and NEAT

Authors: Paul Templier, Lucas Hervier, Dennis G. Wilson

Published in Competition at GECCO 2020, 2020

In this work we present an evolution-based approach applied to Dota 2 in the Project Breezy challenge. The goal of this project is to train an agent to play a 1v1 Midlane match against the game's bots of varrying difficulties, with both sides playing Shadow Fiend. The approach we implemented relies on the MAP-elites algorithm assisted with a neural-based simulator of the game to increase behavior diversity and reduce computation load, using CGP agents or NEAT networks as individuals.