125 lines
3.0 KiB
Python
125 lines
3.0 KiB
Python
import numpy as np
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from a_player import APlayer
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from numpy.random import uniform
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from state import get_potential_moves
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class MachinePlayer(APlayer):
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class Params:
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def __init__(self, p_explore, alpha):
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self.p_exp = p_explore
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self.alpha = alpha
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def __init__(self, mark, params: Params, values):
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APlayer.__init__(self, mark)
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self.params = params
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self.values = {}
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self.with_debug = False
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self.values = values
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self.episode_history = None
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def set_debug(self, with_debug):
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self.with_debug = with_debug
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def print_state_table(self):
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count = 0
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for key in self.values:
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print(f"{self.mark}: {count:05d}: {key} = {self.values[key]:0.3f}")
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count += 1
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@staticmethod
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def to_key(state: np.array):
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key = ''
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for st in state.reshape(state.size):
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key += st
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return key
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def get_value(self, state: np.array):
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key = self.to_key(state)
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try:
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result = self.values[key]
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except KeyError:
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result = 0.5
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return result
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def set_value(self, state: np.array, value):
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self.values[self.to_key(state)] = value
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def end_game(self, value):
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self.set_value(self.state, value)
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print(self.episode_history)
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def new_game(self):
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self.state = None
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self.episode_history = []
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def learn_from_history(self):
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pass
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def calc_value(self, state: np.array, next_state: np.array):
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v0 = self.get_value(state)
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v1 = self.get_value(next_state)
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d = v0 + self.params.alpha*(v1-v0)
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if self.with_debug:
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print(f"{self.mark}: Learned {d:0.3f}")
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return d
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def get_best_move(self, state: np.array, moves: np.array):
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best_move = None
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best_value = -1
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values = np.array([])
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for move in moves:
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# create hypothetical next state
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state_next = self.to_state(state.copy(), move)
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# evaluate value
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value = self.get_value(state_next)
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if best_value < value:
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best_value = value
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best_move = move
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values = np.append(values, value)
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return best_move, best_value, values
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def move(self, state: np.array):
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# get possible move
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moves = get_potential_moves(state)
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if moves.size == 0:
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return state, False
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best_move, best_value, values = self.get_best_move(state, moves)
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next_move = best_move
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is_exp = False
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# Randomly perform exploratory move
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if uniform() < self.params.p_exp:
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index = np.random.randint(len(moves))
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next_move = moves[index]
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is_exp = best_move != next_move
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# Finally create next state
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next_state = self.to_state(state.copy(), next_move)
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next_value = self.get_value(next_state)
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# Maintain history
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self.episode_history.append((self.to_key(next_state), next_value, next_move, is_exp))
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if self.with_debug:
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print(f"{self.mark}: Values = {values}")
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print(f"{self.mark}: Moves = {moves+1}")
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print(f"{self.mark}: Best move = {best_move+1}")
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print(f"{self.mark}: Next move = {next_move+1}, is_exp={is_exp}")
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# Learn
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if not is_exp and self.state is not None:
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d = self.calc_value(self.state, next_state)
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if d > 0:
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self.set_value(self.state, d)
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if self.with_debug:
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print(f"{self.mark}: Learned {d:0.3f}")
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self.state = next_state
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return next_state, True
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