mirror of
https://github.com/peter-tanner/Algorithms-Agents-and-Artificial-Intelligence-project-final.git
synced 2024-11-30 15:10:17 +08:00
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
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from abc import ABC, abstractmethod
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import pickle
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import shelve
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from typing import Dict, List, Tuple, Union
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import etc.gamestate as gs
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from etc.messages import MESSAGE_UNDEFINED, Message
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from etc.util import NoCopyShelf, RecursiveDict
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from agents.action_state import ActionState
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class AbstractInfluencerAgent(ABC):
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last_message: Message
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# Number of red opinions [int]
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# Number of blue opinions [int]
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# Number of red followers [int]
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# Blue energy [int - convert from continuous float to discrete]
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state_action_lut: Dict[int, Dict[int, Dict[int, Dict[int, Dict[int, List[Union[int, float]]]]]]]
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__state_action_lut: NoCopyShelf
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short_term_mem: List[ActionState]
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def __init__(self, state_action_lut_path: str) -> None:
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self.last_message = MESSAGE_UNDEFINED
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self.__state_action_lut = NoCopyShelf.open(
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state_action_lut_path,
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protocol=pickle.HIGHEST_PROTOCOL,
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writeback=True # Yes this makes it less performant, but it will be more readable.
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)
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# Create data for new shelve
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try:
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self.__state_action_lut["data"]
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except KeyError:
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self.__state_action_lut["data"] = RecursiveDict()
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self.state_action_lut = self.__state_action_lut["data"]
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self.short_term_mem = []
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@abstractmethod
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def influence(self, state: "gs.GameState", *args, **kwargs) -> None:
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pass
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@abstractmethod
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def smart_influence(self, state: "gs.GameState") -> None:
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pass
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@abstractmethod
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def choices() -> List[Tuple]:
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pass
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@abstractmethod
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def update_short_term_mem(self, old_state: "gs.GameState", resulting_state: "gs.GameState") -> None:
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pass
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@abstractmethod
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def update_lut(self) -> None:
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pass
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def __update_lut_rating__(self, action_state: ActionState, rating: float) -> None:
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# Number of red opinions [int]
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# Number of blue opinions [int]
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# Number of red followers [int]
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# Blue energy [int - convert from continuous float to discrete]
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# Action
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previous_state_ratings: List[int, float] = self.state_action_lut[action_state.n_red_opinion_bin][action_state.n_blue_opinion_bin][
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action_state.n_red_followers_bin][action_state.blue_energy_bin]
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action = action_state.action.id
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if (type(previous_state_ratings[action]) != list):
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previous_state_ratings[action] = [0, 0.0]
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previous_state_ratings[action][0] += 1
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previous_state_ratings[action][1] += rating
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def close_lut(self) -> None:
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self.__state_action_lut.close()
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def sync_lut(self) -> None:
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self.__state_action_lut.sync()
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