General:
  # RNG-seed to sample the same "random" numbers every time, to make the different runs comparable.
  env_seed: 69
  # Individual vs global rewards
  individual_rewards: true
  # The level.txt file to load from marl_factory_grid/levels
  level_name: quadrant
  # Radius of Partially observable Markov decision process
  pomdp_r: 0 # default 3
  # Print all messages and events
  verbose: false
  # Run tests
  tests: false

# In the "clean and bring" Scenario one agent aims to pick up all items and drop them at drop-off locations while all
# other agents aim to clean dirt piles.
Agents:
  # The clean agents
  Wolfgang:
    Actions:
      - Move4
      - Clean
      - Noop
    Observations:
      - Walls
      - Other
      - DirtPiles
      - Self
    Positions:
      - (9,1)
  Reiner:
    Actions:
      - Move4
      - Clean
      - Noop
    Observations:
      - Walls
      - Other
      - DirtPiles
      - Self
    Positions:
      - (9,8) # (9, 4)

Entities:
  DirtPiles:
    coords_or_quantity:  (1, 1), (1,2), (1,3), (2,4), (2,5), (3,6), (4,7), (5,8), (6,8), (7,9), (8,9), (9,9)
    initial_amount: 0.5 # <1 to ensure that the robot which first attempts to clean this field, can remove the dirt in one action
    clean_amount: 1
    dirt_spawn_r_var: 0
    max_global_amount: 12
    max_local_amount: 1

# Rules section specifies the rules governing the dynamics of the environment.
Rules:

  # Utilities
  # This rule defines the collision mechanic, introduces a related DoneCondition and lets you specify rewards.
  # Can be omitted/ignored if you do not want to take care of collisions at all.
  WatchCollisions:
    done_at_collisions: false

  # Done Conditions
  # Define the conditions for the environment to stop. Either success or a fail conditions.
  # The environment stops when all dirt is cleaned
  DoneOnAllDirtCleaned:
  DoneAtMaxStepsReached:
    max_steps: 200