Evolving Killer Robot Tanks: Mastering Tank Combat Through Genetic Programming
Discover the innovative world of tank combat simulation where human players code their tanks in Java using genetic programming principles. This exploration highlights the challenges and successes in developing autonomous killer robots capable of evolving strategies to survive in hostile environments. With roughly 4000 tank control programs online, this system utilizes adaptive fitness functions, allowing tanks to learn and improve from battles. Uncover what it takes to succeed in evolving effective combat strategies and the intricacies of programming behaviors in these digital warriors.
Evolving Killer Robot Tanks: Mastering Tank Combat Through Genetic Programming
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Presentation Transcript
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
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 onRammed onHit Gun Input onScan Base Other actuators
Function Input 1 Input 2 Output Example AFSM 1. Random ignore ignore 0.87 2. Divide Const_1 Const_2 0.5 3. Greater Than Line 1 Line 2 1 4. Normalize Angle Enemy bearing ignore -50 5. Multiply Line 4 Line 3 -50 6. Output Turn Gun Left Line 5 -50 … … … …
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
Function Input 1 Input 2 Output 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 Example Program 0.87 0.5 1 -50 50 1 1 100 0 0 0 0
Position Velocity Heading Energy Gun Heat Useful Constants 1 2 10 90 Enemy Distance Bearing Heading Energy Velocity Inputs
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
