RoboCup 2D Soccer Simulation League, written in Python, for my A.I. class project.
In case you're wondering, this won the class tournament, but its positioning is shitty tho.
Clone this repo:
git clone <git-url>
Assuming you have rcsoccersim on your machine, run it first. Then run the AI agent optionally with a teamname prepended.
rcsoccersim
[TEAM=<NAME>] python main.py
aigent/aima_python/ is just for reference - that's the sample code from Norvig's Artificial Intelligence, A Modern Approach.
aigent/soccerpy/ contains the source for the agent and client. This is sourced from jsontbradshaw/soccerpy under the MIT license. I've modified it to add several convenient methods, but minimal modification within this folder is needed.
aigent/agent_*.py is the real deal - it lets you configure different agent personalities and stategies. For example you can have striker, defender, goalie agents, which are agent_1, agent_2 and agent_3 respectively.
Once you have the agents, you can import a distribution of them, assign them player numbers and configure their starting positions in main.py. This is also the file you run to play.
Read the paper at report/paper.pdf
Below are my notes containing strategies I used, and ideas I had no time to implement.
act if it's your team's ball.
is_before_kick_off() && is_kick_off_us()
Throw in, free kick, penalty, goalkick BEFORE_KICK_OFF = "before_kick_off" PLAY_ON = "play_on" TIME_OVER = "time_over" KICK_OFF_L = "kick_off_l" KICK_OFF_R = "kick_off_r" KICK_IN_L = "kick_in_l" KICK_IN_R = "kick_in_r" FREE_KICK_L = "free_kick_l" FREE_KICK_R = "free_kick_r" CORNER_KICK_L = "corner_kick_l" CORNER_KICK_R = "corner_kick_r" GOAL_KICK_L = "goal_kick_l" GOAL_KICK_R = "goal_kick_r" DROP_BALL = "drop_ball" OFFSIDE_L = "offside_l" OFFSIDE_R = "offside_r"
When your team has the ball.
- advance: if not close enuf
- shoot: if close enuf and clear
When ball is in your half of the field, and enemy's possession
- just swarm them for now
block, evades
on top of predefined conditional actions and heuristics
Fields, per robot want: input = conditions, output = actions
env input:
- distance to goal
- distance to ball
- distance to enemy
- distance to teammate
output actions:
- shoot to goal (scoring is the whole point of the game)
- pass to teammate
- run to position (ball, teammate, enemy, side)
unsupervised pretraining on autoencoder:
- output = input, i.e. input env conds
supervised training:
- set {env, actions} collected in the last round of game, only if result is positive
First define ur env vars n simple heuristics
or skip the pretraining by using a random net first, plug in net n repeat
run a game, each think step collect data of outcome n prev actions, inject to db. game ends, train autoencoder w/ all actions, then train real plug in the net, repeat above for evolution
where to incorp action success: reward n cost? in selection of data, by evolution n natural selection