git clone https://github.com/zeyus/FLAMEGPU2-Prisoners-Dilemma-ABM.git
cd FLAMEGPU2-Prisoners-Dilemma-ABM
python3 -m pip install -r requirements.txt

# upper agent limit ... please make it a square number for sanity
# this is essentially the size of the grid
MAX_AGENT_SPACES: int = 2**18
# starting agent limit
INIT_AGENT_COUNT: int = int(MAX_AGENT_SPACES * 0.16)

# you can set this anywhere between INIT_AGENT_COUNT and MAX_AGENT_COUNT inclusive
# carrying capacity
AGENT_HARD_LIMIT: int = int(MAX_AGENT_SPACES * 0.5)

# how long to run the sim for
STEP_COUNT: int = 100
# TODO: logging / Debugging
WRITE_LOG: bool = True
LOG_FILE: str = f"data/{strftime('%Y-%m-%d %H-%M-%S')}_{RANDOM_SEED}.json"
VERBOSE_OUTPUT: bool = False
DEBUG_OUTPUT: bool = False
OUTPUT_EVERY_N_STEPS: int = 1

# rate limit simulation?
SIMULATION_SPS_LIMIT: int = 0  # 0 = unlimited

# Show agent visualisation
USE_VISUALISATION: bool = True and pyflamegpu.VISUALISATION

# visualisation camera speed
VISUALISATION_CAMERA_SPEED: float = 0.1
# pause the simulation at start
PAUSE_AT_START: bool = False
VISUALISATION_BG_RGB: List[float] = [0.1, 0.1, 0.1]

# should agents rotate to face the direction of their last action?
VISUALISATION_ORIENT_AGENTS: bool = False
# radius of message search grid (broken now from hardcoded x,y offset map)
MAX_PLAY_DISTANCE: int = 1

# Energy cost per step
COST_OF_LIVING: float = 1.0

# Reproduce if energy is above this threshold
REPRODUCE_MIN_ENERGY: float = 100.0
# Cost of reproduction
REPRODUCE_COST: float = 50.0
# Can reproduce in dead agent's space?
# @TODO: if time, actually implement this, for now. no effect (always True)
ALLOW_IMMEDIATE_SPACE_OCCUPATION: bool = True
# Inheritence: (0, 1]. If 0.0, start with default energy, if 0.5, start with half of parent, etc.
REPRODUCTION_INHERITENCE: float = 0.0
# how many children max per step
MAX_CHILDREN_PER_STEP: int = 1

# Payoff for both cooperating
PAYOFF_CC: float = 3.0
# Payoff for the defector
PAYOFF_DC: float = 5.0
# Payoff for cooperating against a defector
PAYOFF_CD: float = -1.0
# Payoff for defecting against a defector
PAYOFF_DD: float = 0.0

# How agents move
AGENT_TRAVEL_STRATEGIES: List[str] = ["random"]
AGENT_TRAVEL_STRATEGY: int = AGENT_TRAVEL_STRATEGIES.index("random")

# Cost of movement / migration
AGENT_TRAVEL_COST: float = 0.5 * COST_OF_LIVING

# Upper energy limit (do we need this?)
MAX_ENERGY: float = 150.0
# How much energy an agent can start with (max)
INIT_ENERGY_MU: float = 50.0
INIT_ENERGY_SIGMA: float = 10.0
# of cours this can be a specific value
# but this allows for 5 moves before death.
INIT_ENERGY_MIN: float = 5.0
# Noise will invert the agent's decision
ENV_NOISE: float = 0.0

# Agent strategies for the PD game
# "proportion" let's you say how likely agents spawn with a particular strategy
AGENT_STRATEGY_COOP: int = 0
AGENT_STRATEGY_DEFECT: int = 1
AGENT_STRATEGY_TIT_FOR_TAT: int = 2
AGENT_STRATEGY_RANDOM: int = 3

# @TODO: fix if number of strategies is not 4 (logging var...)
AGENT_STRATEGIES: dict = {
    "always_coop": {
        "name": "always_coop",
        "id": AGENT_STRATEGY_COOP,
        "proportion": 1 / 4,
    },
    "always_defect": {
        "name": "always_defect",
        "id": AGENT_STRATEGY_DEFECT,
        "proportion": 1 / 4,
    },
    # defaults to coop if no previous play recorded
    "tit_for_tat": {
        "name": "tit_for_tat",
        "id": AGENT_STRATEGY_TIT_FOR_TAT,
        "proportion": 1 / 4,
    },
    "random": {
        "name": "random",
        "id": AGENT_STRATEGY_RANDOM,
        "proportion": 1 / 4,
    },
}

# How many variants of agents are there?, more wil result in more agent colors
AGENT_TRAIT_COUNT: int = 4
# @TODO: allow for 1 trait (implies no strategy per trait)
# AGENT_TRAIT_COUNT: int = 1

# if this is true, agents will just have ONE strategy for all
# regardless of AGENT_STRATEGY_PER_TRAIT setting.
AGENT_STRATEGY_PURE: bool = False
# Should an agent deal differently per variant? (max strategies = number of variants)
# or, should they have a strategy for same vs different (max strategies = 2)
AGENT_STRATEGY_PER_TRAIT: bool = False

# Mutation frequency
AGENT_TRAIT_MUTATION_RATE: float = 0.0


MULTI_RUN = False
MULTI_RUN_STEPS = 10000
MULTI_RUN_COUNT = 1