evolving killer robot tanks

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Evolving Killer Robot Tanks. Jacob Eisenstein. Why Must We Fight?. Giving the people what they want. Essence of embodiment: Moving around and surviving in a hostile environment. Real creatures…. Tank fighting simulator Human players code tanks in Java ~4000 tank control programs online - PowerPoint PPT Presentation

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Evolving Killer Robot Tanks

Jacob Eisenstein

Why Must We Fight?

Giving the people what they want

Essence of embodiment: Moving around and surviving in a hostile environment.

Real creatures…

Tank fighting simulator Human players code tanks in Java ~4000 tank control programs online

Directional “radar” sensor Must be pointed at enemy to see

Actuators Moving, turning takes time Gun must cool before firing

No terrain effects Walled combat area

Dodging Squigbot

Evolving Robocode Tanks

Use genetic programming to evolve tanks Many reports of people trying this… ...no reports of success! Wrong encoding?

Representation

Each AFSM is a REX-like program Fixed-length encoding

64 operations per AFSM ~2000 bits per genome

Input

Base

onRammed

onHit

onScan

Other actuators

Gun

Example AFSM

0.87

0.5

1

-50

-50

-50

1. Random ignore ignore

2. Divide Const_1 Const_2

3. Greater Than Line 1 Line 2

4. Normalize Angle Enemy bearing ignore

5. Multiply Line 4 Line 3

6. Output Turn Gun Left Line 5

Function Input 1 Input 2 Output

… … … …

Training

Scaled fitness Mutation pegged to diversity Typical parameters

200-500 individuals 10% copy, 88% crossover, 2%

elitism This takes a LONG TIME!!!

Sample from ~25 starting positions Up to 50,000 battles per generation 0.2-1.0 seconds per battle 20 minutes to 3 hours per

generation

Results Fixed starting position, one

opponent GP crushes all opposition Beats “showcase” tank

Randomized starting positions Wins 80% of battles against

“learning” tank Wins 50% against “showcase”

tank Multiple opponents

Beats 4 out of 5 “learning” tanks

Both… Unsuccessful

GP is not Magic

A good encoding provides a huge advantage. Previous researchers got this wrong

GP is really good at finding non-general solutions Clever fitness functions can encourage general

solutions Much more computationally expensive

Cornering CornerBot

Example Program

0.87

0.5

1

-50

50

1

1

100

0

0

0

1. Random ignore ignore

2. Divide Const_1 Const_2

3. Greater Than Line 1 Line 2

4. Normalize Angle Enemy bearing ignore

5. Absolute Value Line 4 ignore

6. Less Than Line 4 Const_90

7. And Line 6 Line 3

8. Multiply Const_10 Const_10

9. Less Than Enemy distance Line 8

10. And Line 9 Line 7

11. Multiply Line 10 Line 4

12. Output Turn gun left Line 11 0

Function Input 1 Input 2 Output

Inputs Position Velocity Heading Energy Gun Heat Useful Constants

1 2 10 90

Enemy Distance Bearing Heading Energy Velocity

Outputs

Forward / Backward Turn robot Turn radar Turn gun Fire

Gun heat must be zero Variable power

Functions

Greater than, less than, equal + - * / % Absolute value Random number Constant And, or, not Normalize relative angle

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