import streamlit as st import numpy as np import Arena from MCTS import MCTS from no_one.NoOneGame import NoOneGame from no_one.pytorch.NNet import NNetWrapper as NNet from utils import * import time square_content = { -1: "❌", +0: "·", +1: "⭕" } players = [{"name": "❌"}, {"name": "⭕"}] player_types = [":rainbow[AI]", "Human"] ai_ranks = ["Super", "First Blood", "Random"] ai_models = { "Super": "super.pth.tar", "First Blood": "firstBlood.pth.tar", "Random": "random.pth.tar", } game = NoOneGame(4) def check_clicked(i, j): if st.session_state.clicked == (i, j): return True return False class HumanPlayer: def __init__(self, game): self.game = game def encode(self, src, target): row, col = src[0] - target[0], src[1] - target[1] if (row, col) == (0, -1): d = 1 elif (row, col) == (0, 1): d = 3 elif (row, col) == (-1, 0): d = 0 elif (row, col) == (1, 0): d = 2 else: return None return self.game.encodeAction(src, d) def play(self, board, player, src, target): action = self.encode(src, target) if action is None: return None valids = self.game.getValidMoves(board, player) if valids[action] != 1: return None return action def ai_player(ai_model): n1 = NNet(game) n1.load_checkpoint('./models/', ai_models[ai_model]) args1 = dotdict({'numMCTSSims': 50, 'cpuct':1.0}) msts1 = MCTS(game, n1, args1) return lambda x: np.argmax(msts1.getActionProb(x, temp=0)) def human_step(i, j): if st.session_state.clicked is None: if st.session_state.board[i, j] != st.session_state.player: st.toast('Invalid move!') return st.session_state.clicked = (i, j) return elif st.session_state.clicked == (i, j): # selection canceled st.session_state.clicked = None return elif st.session_state.board[i, j] == st.session_state.player: st.session_state.clicked = (i, j) return else: # move player = st.session_state.players[st.session_state.player + 1] action = player.play(st.session_state.board, st.session_state.player, st.session_state.clicked, (i, j)) if action is None: st.toast('Invalid move!') return move(action) st.session_state.clicked = None def ai_step(): p = st.session_state.player action = st.session_state.players[p+1](game.getCanonicalForm(st.session_state.board, st.session_state.player)) # st.session_state.board, st.session_state.player = game.getNextState( # st.session_state.board, p, action, # ) move(action) def move(action): p = st.session_state.player st.session_state.board, st.session_state.player = game.getNextState( st.session_state.board, p, action, ) res = game.getGameEnded(st.session_state.board, p) if res != 0: st.session_state.winner = res*p st.session_state.win[res*p] += 1 st.balloons() def reinit(): st.session_state.reinit = True def init(post_init=False): st.session_state.reinit = False if not post_init: st.session_state.win = {-1: 0, 1: 0} st.session_state.board = game.getInitBoard() st.session_state.player = -1 st.session_state.players = [None for i in range(2)] for i, setting in enumerate(st.session_state.player_settings): if setting["pt"] == player_types[1]: st.session_state.players[i] = HumanPlayer(game) else: st.session_state.players[i] = ai_player(setting["ai_model"]) st.session_state.players.insert(1, None) st.session_state.winner = None st.session_state.clicked = None def player_to_index(p): if p == -1: return 0 elif p == 1: return 1 def main(): if "player_settings" not in st.session_state: st.session_state.player_settings = [ {"pt": player_types[1], "ai_model": ai_ranks[0]}, {"pt": player_types[0], "ai_model": ai_ranks[1]}, ] if "reinit" not in st.session_state: st.session_state.reinit = False fire, settings = st.columns([1, 2]) fire.button('New Game', on_click=init, args=(True,)) with settings.expander( 'Settings', expanded=False, ): st.warning('Any setting changing will restart the game immediately', icon="⚠️") for i, p in enumerate(st.columns([0.5, 0.5])): with p: # players[i] st.session_state.player_settings[i]["pt"] = st.radio( f"Who will play %s" % players[i]["name"], player_types, key=f"xp_type_{i}", horizontal=True, index=player_types.index(st.session_state.player_settings[i]["pt"]), on_change=reinit, ) if st.session_state.player_settings[i]["pt"] == player_types[0]: st.session_state.player_settings[i]["ai_model"] = st.radio( "AI rank", ai_ranks, key=f"xp_rank_{i}", index=ai_ranks.index(st.session_state.player_settings[i]["ai_model"]), on_change=reinit, ) st.divider() st.checkbox("Show me how to play", key="show_how_to_play") if "board" not in st.session_state or st.session_state.reinit: init() if st.session_state.show_how_to_play: with st.sidebar: st.title("How to play") how_to = '''1. 游戏的目标是吃掉对方的棋子 开局双方各有四枚棋子，被吃剩一枚棋子即可判负 2. 棋子可以向上下左右移动到空白位置 3. 如何吃子 1. 移动后的棋子需要跟本方其他某个棋子，在水平方向，或者垂直方向上连住 2. 如果连住后的棋子两端有对方一个棋子，那么这个对方的棋子就被吃掉 3. 如果对方也有两颗棋子，则互相不吃 4. 如果落子在对方连起来的两枚棋子一端，也不会被吃 5. 如果选择 AI vs AI，需要手动点击比分下面的回合按钮，触发下一次落子 ''' st.markdown(how_to) xp, score, op = st.columns([2, 8, 2]) for i, p in enumerate([xp, op]): p.title(players[i]["name"], anchor=False) caption = st.session_state.player_settings[i]["pt"] if st.session_state.player_settings[i]["pt"] == player_types[0]: caption = st.session_state.player_settings[i]["ai_model"] + " " + caption p.caption(caption) # st.session_state if st.session_state.player_settings[player_to_index(st.session_state.player)]['pt'] != "Human" and st.session_state.winner is None: ai_step() st.divider() for i, row in enumerate(st.session_state.board): cols = st.columns([5, 1, 1, 1, 1, 5]) for j, field in enumerate(row): cols[j + 1].button( square_content[field], key=f"{i}-{j}", type=f'{"primary" if check_clicked(i, j) else "secondary"}', on_click=human_step, args=(i, j), ) s = score.columns([2, 2, 2]) s[1].title(f'{st.session_state.win[-1]} : {st.session_state.win[1]}', anchor=False) s[1].button( f'{"❌" if st.session_state.player == -1 else "⭕"}\'s turn' if not st.session_state.winner else f'🏁 Game finished' ) if __name__ == '__main__': main()