AzulMARL

Most important libraries used

Usage

Initiating the env via PettingZoo

from azul_marl_env import azul_v1_2players, azul_v1_3players, azul_v1_4players
env_2players = azul_v1_2players()
env_3players = azul_v1_3players()
env_4players = azul_v1_4players()
env_2players_custom_max_moves = azul_v1_2players(max_moves=100)

Initiating the env directly

from azul_marl_env import AzulEnv
env = AzulEnv(player_count=2)
env = AzulEnv(player_count=3)
env = AzulEnv(player_count=4) 
env = AzulEnv(player_count=2, max_moves=100)

Making moves

from azul_marl_env import azul_v1_2players
import random
# Create and reset the environment
env = azul_v1_2players()
observation, info = env.reset()
# Iterate through agents
for agent in env.agent_iter():
 # Get current agent's observation and info
 observation, reward, termination, truncation, info = env.last()
 
 if termination or truncation:
 break
 
 # Get valid moves for current agent
 valid_moves = info["valid_moves"]
 # Select a random valid move
 action = random.choice(valid_moves)
 # Execute the move
 env.step(action)
 
 # Render the environment (optional)
 env.render()
# Close the environment
env.close()

Example of a complete game using random valid moves

from azul_marl_env import azul_v1_2players
import random
def play_random_game():
 env = azul_v1_2players()
 observation, info = env.reset()
 
 for agent in env.agent_iter():
 observation, reward, termination, truncation, info = env.last()
 
 if termination or truncation:
 print(f"Game finished! Final scores: {[player['score'] for player in observation['players']]}")
 break
 
 # Get valid moves and make a random move
 valid_moves = info["valid_moves"]
 if valid_moves:
 action = random.choice(valid_moves)
 env.step(action)
 
 env.close()
play_random_game()

Environment Details

Factory count (num_factories):
 2 player game -> 5
 3 player game -> 7
 4 player game -> 9