mirror of
https://github.com/peter-tanner/Algorithms-Agents-and-Artificial-Intelligence-project-final.git
synced 2024-11-30 09:00:17 +08:00
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
|
from abc import ABC, abstractmethod
|
||
|
|
import pickle
|
||
|
|
import shelve
|
||
|
|
from typing import Dict, List, Tuple, Union
|
||
|
|
|
||
|
|
import etc.gamestate as gs
|
||
|
|
from etc.messages import MESSAGE_UNDEFINED, Message
|
||
|
|
from etc.util import NoCopyShelf, RecursiveDict
|
||
|
|
from agents.action_state import ActionState
|
||
|
|
|
||
|
|
|
||
|
|
class AbstractInfluencerAgent(ABC):
|
||
|
|
last_message: Message
|
||
|
|
|
||
|
|
# Number of red opinions [int]
|
||
|
|
# Number of blue opinions [int]
|
||
|
|
# Number of red followers [int]
|
||
|
|
# Blue energy [int - convert from continuous float to discrete]
|
||
|
|
state_action_lut: Dict[int, Dict[int, Dict[int, Dict[int, Dict[int, List[Union[int, float]]]]]]]
|
||
|
|
__state_action_lut: NoCopyShelf
|
||
|
|
|
||
|
|
short_term_mem: List[ActionState]
|
||
|
|
|
||
|
|
def __init__(self, state_action_lut_path: str) -> None:
|
||
|
|
self.last_message = MESSAGE_UNDEFINED
|
||
|
|
self.__state_action_lut = NoCopyShelf.open(
|
||
|
|
state_action_lut_path,
|
||
|
|
protocol=pickle.HIGHEST_PROTOCOL,
|
||
|
|
writeback=True # Yes this makes it less performant, but it will be more readable.
|
||
|
|
)
|
||
|
|
# Create data for new shelve
|
||
|
|
try:
|
||
|
|
self.__state_action_lut["data"]
|
||
|
|
except KeyError:
|
||
|
|
self.__state_action_lut["data"] = RecursiveDict()
|
||
|
|
self.state_action_lut = self.__state_action_lut["data"]
|
||
|
|
|
||
|
|
self.short_term_mem = []
|
||
|
|
|
||
|
|
@abstractmethod
|
||
|
|
def influence(self, state: "gs.GameState", *args, **kwargs) -> None:
|
||
|
|
pass
|
||
|
|
|
||
|
|
@abstractmethod
|
||
|
|
def smart_influence(self, state: "gs.GameState") -> None:
|
||
|
|
pass
|
||
|
|
|
||
|
|
@abstractmethod
|
||
|
|
def choices() -> List[Tuple]:
|
||
|
|
pass
|
||
|
|
|
||
|
|
@abstractmethod
|
||
|
|
def update_short_term_mem(self, old_state: "gs.GameState", resulting_state: "gs.GameState") -> None:
|
||
|
|
pass
|
||
|
|
|
||
|
|
@abstractmethod
|
||
|
|
def update_lut(self) -> None:
|
||
|
|
pass
|
||
|
|
|
||
|
|
def __update_lut_rating__(self, action_state: ActionState, rating: float) -> None:
|
||
|
|
# Number of red opinions [int]
|
||
|
|
# Number of blue opinions [int]
|
||
|
|
# Number of red followers [int]
|
||
|
|
# Blue energy [int - convert from continuous float to discrete]
|
||
|
|
# Action
|
||
|
|
previous_state_ratings: List[int, float] = self.state_action_lut[action_state.n_red_opinion_bin][action_state.n_blue_opinion_bin][
|
||
|
|
action_state.n_red_followers_bin][action_state.blue_energy_bin]
|
||
|
|
action = action_state.action.id
|
||
|
|
if (type(previous_state_ratings[action]) != list):
|
||
|
|
previous_state_ratings[action] = [0, 0.0]
|
||
|
|
previous_state_ratings[action][0] += 1
|
||
|
|
previous_state_ratings[action][1] += rating
|
||
|
|
|
||
|
|
def close_lut(self) -> None:
|
||
|
|
self.__state_action_lut.close()
|
||
|
|
|
||
|
|
def sync_lut(self) -> None:
|
||
|
|
self.__state_action_lut.sync()
|