269 lines
7.0 KiB
Python
269 lines
7.0 KiB
Python
import numpy as np
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from numpy.random import uniform
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float_formatter = "{:.3f}".format
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np.set_printoptions(formatter={'float_kind': float_formatter})
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def softmax(values: np.array, eps=1.0E-6) -> np.array:
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result = (values + eps) / (sum(values + eps))
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return result
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def sample(values: np.array, with_debug=False):
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# Normalize and sort
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probs = softmax(values)
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sorted_indices = np.argsort(probs)
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sorted_probs = probs[sorted_indices]
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z = uniform()
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if with_debug:
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print(f"Probs={probs}")
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print(f"Z={z:.3f}")
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index = None
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p_sum = 0
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for idx, p in enumerate(sorted_probs):
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p_sum += p
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if z <= p_sum:
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index = sorted_indices[idx]
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break
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return index
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def test_sample(data):
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p = np.array(data)
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c = np.array([0]*len(data))
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for i in range(0, 1000):
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index = sample(p, with_debug=False)
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c[index] += 1
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print(c)
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def to_state_string(state, state_nex=None):
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sp = ' '
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sp_arrow = ' => '
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sp_ = [sp, sp_arrow, sp]
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def col(str_in, state):
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str_out = str_in
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for c in range(0, 3):
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char = state[3*r+c]
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if char == '-':
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char = ' '
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str_out += "|" + char
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str_out += '|'
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return str_out
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state_str = ''
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for r in range(0, 3):
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state_str = col(state_str, state)
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if state_nex is not None:
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state_str += sp_[r]
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state_str = col(state_str, state_nex)
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if r != 2:
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state_str += '\x0A'
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return state_str
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class APlayer(object):
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def __init__(self, mark):
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self.mark = mark
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self.state = None
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self.state_opp = None
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self.state_last = None
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def move(self, state):
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return state, False
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def has_won(self, state):
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ref = self.mark + self.mark + self.mark
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substr = state[0:3]
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if substr in ref:
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return True
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substr = state[3:6]
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if substr in ref:
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return True
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substr = state[6:9]
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if substr in ref:
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return True
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substr = state[0:9:3]
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if substr in ref:
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return True
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substr = state[1:9:3]
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if substr in ref:
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return True
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substr = state[2:9:3]
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if substr in ref:
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return True
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substr = state[0:9:4]
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if substr in ref:
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return True
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substr = state[6:0:-2]
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if substr in ref:
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return True
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return False
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class MachinePlayer(APlayer):
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def __init__(self, mark='X', with_debug=False, values=None):
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APlayer.__init__(self, mark)
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self.p_exp = 0.2
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self.alpha = 0.9
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self.with_debug = with_debug
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if values is None:
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self.values = {}
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else:
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self.values = values
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def get_value(self, state):
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try:
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result = self.values[state]
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except KeyError:
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result = 0
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return result
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def set_value(self, state, value):
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self.values[state] = value
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def reward(self, value):
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self.set_value(self.state_last, value)
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@staticmethod
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def get_potential_moves(state) -> np.array:
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indices = [idx for idx, s in enumerate(state) if '-' in s]
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return np.array(indices)
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def move(self, state):
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values = np.array([])
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# get possible move
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moves = self.get_potential_moves(state)
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can_move = moves.size > 0
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self.state_opp = state
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if can_move:
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for move in moves:
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# create hypothetical next state
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state_next = self.state_from_move(state, move)
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# evaluate value
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value = self.get_value(state_next)
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values = np.append(values, value)
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is_exp = False
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if uniform() < self.p_exp:
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is_exp = True
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index = np.random.randint(len(moves))
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else:
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index = sample(values)
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move = moves[index]
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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}: Move = {move+1}, is_exp={is_exp}")
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self.state_last = self.state
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self.state = self.state_from_move(state, move)
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# Learn
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if not is_exp:
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v0 = self.get_value(self.state_last)
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v1 = self.get_value(self.state)
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d = max(0, v1-v0)
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if d > 0:
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self.set_value(self.state_last, self.alpha*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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return self.state, can_move
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def state_from_move(self, state, field):
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return state[:field] + self.mark + state[field + 1:]
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class PlayerProvider:
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def __init__(self, p1: APlayer, p2: APlayer):
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self.p1 = p1
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self.p2 = p2
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def choose_player(player_provider: PlayerProvider) -> list[APlayer]:
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# Wer fängt an?
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p1 = player_provider.p1
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p2 = player_provider.p2
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if uniform() < 0.5:
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result = [p1, p2]
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else:
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result = [p2, p1]
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return result
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def play(player_provider: PlayerProvider, k_max=10000, with_print=False):
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for k in range(0, k_max):
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players = choose_player(player_provider)
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state = "---------"
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move = 1
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run = True
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while run:
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for player in players:
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if with_print:
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print(f"---------------------------------------------------")
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print(f"- Game {k:06d}, Move {move} -----------------------------")
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print(f"---------------------------------------------------")
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last_state = state
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state, has_moved = player.move(state)
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if with_print:
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print(to_state_string(last_state, state))
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if not has_moved:
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if with_print:
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print(f"{player.mark}: No more moves")
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run = False
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if player.has_won(state):
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if with_print:
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print(f"{player.mark}: Has won the game")
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if isinstance(player, MachinePlayer):
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player.reward(1.0)
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run = False
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if not run:
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break
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move += 1
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px = MachinePlayer(mark='X', with_debug=False)
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po = MachinePlayer(mark='O', with_debug=False)
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do_training = 1
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if do_training:
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players = PlayerProvider(px, po)
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play(players, 20000, False)
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players = PlayerProvider(MachinePlayer(mark='X', with_debug=True, values=px.values), MachinePlayer(mark='O', with_debug=True, values=po.values))
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play(players, 100, True)
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print("Testing")
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test_sample([0.7, 0.1, 0.1, 0.1])
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test_sample([0.1, 0.1, 0.2, 0.1])
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test_sample([0.2, 0.8])
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test_sample([0.1, 0.1])
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test_sample([0.3, 0.0, 0.2, 0.0])
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test_sample([0.0, 0.0, 0.0, 0.0])
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test_sample([0.5, 0.0, 0.2, 0.3])
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test_sample([0.3, 0.1, 0.4, 0.2])
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