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This talk by Dr. Kenneth Stanley explores the challenges and solutions for implementing real-time NeuroEvolution of Augmenting Topologies (NEAT), focusing on steady state evolution and simultaneous evaluation. The talk covers topics such as replacing individuals one at a time, dynamic compatibility thresholding, and the implementation of NEAT in the NERO game. The talk concludes with future applications and improving the neural model.
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CAP6938Neuroevolution and Developmental EncodingReal-time NEAT Dr. Kenneth Stanley October 18, 2006
Generations May Not Always Be Appropriate • When a population is evaluated simultaneously • Many are observable at the same time • Therefore, entire population would change at once • A sudden change is incongruous, highly noticeable • When a human interacts with one individual at a time • Want things to improve constantly
Steady State GA: One Individual Is Replaced at a Time • Start by evaluating entire first generation • Then continually pick one to remove, replace it with child of the best Start: Evaluate All 2) Create offpsring from good parents Repeat… 3) Replace removed individual 1) Remove poor individual
Steady State During Simultaneous Evaluation: Similar but not Identical • Several new issues when evolution is real-time • Evaluation is asynchronous • When to replace? • How to assign fitness? • How to display changes
Regular NEAT Introduces Additional Challenges for Real Time • Speciation equations based on generations • No “remove worst” operation defined in algorithm • Dynamic compatibility thresholding assumes generations
How to Remove the Worst? • No such operation in generational NEAT • Worst often may often be a new species • Removing it would destroy protection of innovation • Loss of regular NEAT dynamics
Dynamic Compatibility Thresholding Assumes A Next Generation
Real-time NEAT Addresses Both the Steady State and Simultaneity Issues • Real-time speciation • Simultaneous and asynchronous evaluation • Steady state replacement • Fast enough to change while a game is played • Equivalent dynamics to regular NEAT
Finally: How Many Ticks Between Replacements? • Intuitions: • The more often replacement occurs, the fewer are eligible • The larger the population, the more are eligible • The high the age of maturity, the fewer are eligible
rtNEAT Is Implemented In NERO • Download at http://nerogame.org • rtNEAT source available • Simulated demos have public appeal • Over 70,000 downloads • Appeared on Slashdot • Best Paper Award in Computational Intelligence and Games • Independent Games Festival Best Student Game Award • rtNEAT licensed • Worldwide media coverage
NERO: NeuroEvolving Robotic Operatives • NPCs improve in real time as game is played • Player can train AI for goal and style of play • Each AI Unit Has Unique NN
NERO Battle Mode • After training, evolved behaviors are saved • Player assembles team of trained agents • Team is tested in battle against opponent’s team
NERO Training: The Factory • Reduces noise during evaluation • All evaluations start out similarly • Robot bodies produced by “factory” • Each body sent back to factory to respawn • Bodies retain their NN unless chosen for replacement • NN’s have different ages • Fitness is diminishing average of spawn trials:
Further Applications? • New kinds of games • New kinds of AI in games • New kinds of real-time simulations • Training applications • Interactive steady-state evolution
Next Topic: Improving the neural model • Adaptive neural networks • Change over a lifetime • Leaky integrator neurons and CTRNN Evolutionary Robots with On-line Self-Organization and Behavioral Fitness by Dario Floreano and Joseba Urzelai (2000)Evolving Adaptive Neural Networks with and Without Adaptive Synapses by Kenneth O. Stanley, Bobby D. Bryant, and Risto Miikkulainen (2003)
