Evolving Multimodal Networks for Multitask Games Jacob Schrum – schrum2@cs.utexas.edu Risto Miikkulainen – risto@cs.utexas.edu University of Texas at Austin Department of Computer Science

Evolution in videogames Automatically learn interesting behavior Complex but controlled environments Stepping stone to real world Robots Training simulators Complexity issues Multiple contradictory objectives Multiple challenging tasks

Multitask Games NPCs perform two or more separate tasks Each task has own performance measures Task linkage Independent Dependent Not blended Inherently multiobjective

Test Domains Designed to study multimodal behavior Two tasks in similar environments Different behavior needed to succeed Main challenge: perform well in both Front Ramming Back Ramming

Front/Back Ramming Same goal, opposite embodiments Front Ramming Attack w/front ram Avoid counterattacks Back Ramming Attack w/back ram Avoid counterattacks

Predator/Prey Same embodiment, opposite goals Predator Prey Attack prey Prevent escape Prey Avoid attack Stay alive

Multiobjective Optimization High health but did not deal much damage Game with two objectives: Damage Dealt Remaining Health A dominates B iff A is strictly better in one objective and at least as good in others Population of points not dominated are best: Pareto Front Weighted-sum provably incapable of capturing non-convex front Tradeoff between objectives Dealt lot of damage, but lost lots of health

NSGA-II Evolution: natural approach for finding optimal population Non-Dominated Sorting Genetic Algorithm II* Population P with size N; Evaluate P Use mutation to get P´ size N; Evaluate P´ Calculate non-dominated fronts of {P È P´} size 2N New population size N from highest fronts of {P È P´} *K. Deb et al. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. Evol. Comp. 2002

Constructive Neuroevolution Genetic Algorithms + Neural Networks Build structure incrementally (complexification) Good at generating control policies Three basic mutations (no crossover used) Perturb Weight Add Connection Add Node

Multimodal Networks (1) Multitask Learning* One mode per task Shared hidden layer Knows current task Previous work Supervised learning context Multiple tasks learned quicker than individual Not tried with evolution yet * R. A. Caruana, "Multitask learning: A knowledge-based source of inductive bias" ICML 1993

Multimodal Networks (2) Starting network with one mode Mode Mutation Extra modes evolved Networks choose mode Chosen via preference neurons MM Previous Links from previous mode Weights = 1.0 MM Random Links from random sources Random weights Supports mode deletion MM(P) MM(R)

Experiment Compare 4 conditions: 500 generations Population size 52 Control: Unimodal networks Multitask: One mode per task MM(P): Mode Mutation Previous MM(R): Mode Mutation Random + Delete Mutation 500 generations Population size 52 “Player” behavior scripted Network controls homogeneous team of 4

MO Performance Assessment Reduce Pareto front to single number Hypervolume of dominated region Pareto compliant Front A dominates front B implies HV(A) > HV(B) Standard statistical comparisons of average HV

20 runs

Front/Back Ramming Behaviors Multitask Front Ramming Back Ramming MM(R)

20 runs

Predator/Prey Behaviors Multitask Prey Predator MM(R)

Discussion (1) Front/Back Ramming Control < MM(P), MM(R) < Multitask Multiple modes help Explicit knowledge of task helps

Discussion (2) Predator/Prey MM(P), Control, Multitask < MM(R) Multiple modes not necessarily helpful Disparity in relative difficulty of tasks Multitask ends up wasting effort Mode deletion aids search for one good mode

How To Apply Multitask good if: Mode mutation good if: Task division known, and Tasks are comparably difficult Mode mutation good if: Task division is unknown, or “Obvious” task division is misleading

Future Work Games with more tasks Games with independent tasks Does method scale? Control mode bloat Games with independent tasks Ms. Pac-Man Collect pills while avoiding ghosts Eat ghosts after eating power pill Games with blended tasks Unreal Tournament 2004 Fight while avoiding damage Fight or run away? Collect items or seek opponents?

Conclusion Domains with multiple tasks are common Both in real world and games Multimodal networks improve learning in multitask games Will allow interesting/complex behavior to be developed in future

Questions? Jacob Schrum – schrum2@cs.utexas.edu Risto Miikkulainen – risto@cs.utexas.edu University of Texas at Austin Department of Computer Science

Auxiliary Slides