CAP6938Neuroevolution and Artificial Embryogeny

Real-time NEAT

Dr. Kenneth Stanley

February 22, 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

1f

2f

3f

4f

5f

6f

7f

8f

1) Remove poor individual

2) Create offpsring from good parents

3) Replace removed individual

Repeat…

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

Speciation Equations Based on 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

Main Loop (Non-Generational)

Choosing the Parent Species

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 soon available (TBA)• Simulated demos have public appeal

– Over 50,000 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:

NERO Inputs and Outputs

Enemy/Friend Radars

Enemy On-Target Sensor

Object Rangefinder Sensors

Enemy Line-of-Fire Sensors

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

Homework due 2/27/06: Working genotype to phenotype mapping. Genetic representation completed. Saving and loading of genome file I/O functions completed. Turn in summary, code, and examples demonstrating that it works.

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)

Project Milestones (25% of grade)

• 2/6: Initial proposal and project description• 2/15: Domain and phenotype code and examples• 2/27: Genes and Genotype to Phenotype mapping • 3/8: Genetic operators all working• 3/27: Population level and main loop working• 4/10: Final project and presentation due (75% of grade)