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Incremental Coevolution With Competitive and Cooperative Tasks in a Multirobot Environment

Eiji Uchibe, Okinawa Institute of Science Minoru Asada, Osaka University Proceedings of the IEEE, July 2006. Incremental Coevolution With Competitive and Cooperative Tasks in a Multirobot Environment. Presented By: Dan DeBlasio For : CAP 6671, Spring 2008 8 April 2008. Coevolution.

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Incremental Coevolution With Competitive and Cooperative Tasks in a Multirobot Environment

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  1. Eiji Uchibe, Okinawa Institute of Science Minoru Asada, Osaka University Proceedings of the IEEE, July 2006 Incremental Coevolution WithCompetitive and CooperativeTasks in a MultirobotEnvironment Presented By: Dan DeBlasio For : CAP 6671, Spring 2008 8 April 2008

  2. Coevolution • Two (or more) separate populations • Evolve the populations separately • Creates “arms race”

  3. Competitive v. Cooperative • Have agents from each population compete to gain fitness • Usually one is being evaluated at a time • Agents from multiple populations work together to solve a problem • Team evaluated as a whole, not each agent

  4. Robocup • Special because you need both cooperative and competitive components • Groups of agents need to work together as a team (cooperation) • Need to defeat the other team (competitive)

  5. Paper v. My Work • Presented for a small league team • 3 agents per team • Work done on a simulation league • Up to 11 players per team

  6. Motivation • Evaluation is a big issue • Even with two populations of 100 agents, to accurately evaluate each player in population A, it would need to play each agent in B • That would be 10,000 simulated games per generation

  7. How do we reduce the number of games per generation, without degrading the results if our fitness evaluation?

  8. Fitness Sharing • At each iteration, agents are selected from each population to actually control the players • After evaluation of each agent, the system updates the fitness value of each agent in the population using its similarity to the agent that was selected.

  9. Fitness Sharing

  10. Fitness Sharing • Each Individual in population has: • π - policy (brain) • v - (previous)performance value • f - fitness

  11. Fitness Sharing • At the end of each generation, the individuals not selected to participated are assigned fitness as follows: f is calculated using the similarity of j to the selected player on each game w (similarity) is calculated by seeing if each action state pair would have happened in j as it did in the game l

  12. Policy Representation • Leaves are simple executions (kick, pass, run) • Branches contain an object and a description • If true, go left

  13. Genetic Manipulation • Basic GP manipulation is used • Crossover • Select two points from parents trees, swap subtrees • Mutation • Change random action, object, or description in tree • Add new branch

  14. Selection

  15. Selection • v is calculated for each individual as follows: Here ~f is a random number between 0 and the minimum fitness in the set of best Also j1 and j2 are the parents selected in crossover

  16. Evolution Schedule • 3 robot environment • Keeper • Shooter • passer

  17. Evolution Schedule • Cooperative Schedule • Train mainly the shooter and passer to work together • Keeper does not get much playing time • Competitive schedule • Keeper and shooter are evolved • Passer is left out much of the time • No Schedule • All three play all the time

  18. Evolution Schedule • Multiple Schedules • For each game select stage from the other three schedule types

  19. Results Average Fitness using different schedules

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