# agents.py - Agent and Controllers
# AIFCA Python code Version 0.9.7 Documentation at http://aipython.org
# Download the zip file and read aipython.pdf for documentation

# Artificial Intelligence: Foundations of Computational Agents http://artint.info
# Copyright 2017-2023 David L. Poole and Alan K. Mackworth
# This work is licensed under a Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# See: http://creativecommons.org/licenses/by-nc-sa/4.0/deed.en

from display import Displayable  

class Agent(Displayable):

    def initial_action(self, percept):
        """return the initial action"""
        raise NotImplementedError("go")   # abstract method

    def select_action(self, percept):
        """return the next action (and update internal state) given percept
        percept is variable:value dictionary
        """
        raise NotImplementedError("go")   # abstract method

class Environment(Displayable):
    def initial_percept(self):
        """returns the initial percept for the agent"""
        raise NotImplementedError("initial_percept")   # abstract method

    def do(self, action):
        """does the action in the environment
        returns the next percept """
        raise NotImplementedError("Environment.do")   # abstract method

class Simulate(Displayable):
    """simulate the interaction between the agent and the environment
    for n time steps.
    Returns a pair of the agent state and the environment state.
    """
    def __init__(self,agent, environment):
        self.agent = agent
        self.env = environment
        self.percept = self.env.initial_percept()
        self.percept_history = [self.percept]
        self.action_history = []
        
    def go(self, n):
        for i in range(n):
            action = self.agent.select_action(self.percept)
            self.display(2,f"i={i} action={action}")
            self.percept = self.env.do(action)
            self.display(2,f"      percept={self.percept}")