基于Python+Pygame+PaddlePaddle打造一款点击按钮就能可视化地训练围棋人工智能的程序+代码+文档说明

# About An environment for the board game Go. It is implemented using OpenAI's Gym API. It is also optimized to be as efficient as possible in order to efficiently train ML models. # Installation ```bash # In the root directory pip install -e . ``` # API ### Basic example ```bash # In the root directory python3 demo.py ```  ### Coding example ```python import gym go_env = gym.make('gym_go:go-v0', size=7, komi=0, reward_method='real') first_action = (2,5) second_action = (5,2) state, reward, done, info = go_env.step(first_action) go_env.render('terminal') ``` ``` 0 1 2 3 4 5 6 ----------------------------- 0 | | | | | | | | ----------------------------- 1 | | | | | | | | ----------------------------- 2 | | | | | | B | | ----------------------------- 3 | | | | | | | | ----------------------------- 4 | | | | | | | | ----------------------------- 5 | | | | | | | | ----------------------------- 6 | | | | | | | | ----------------------------- Turn: WHITE, Last Turn Passed: False, Game Over: False Black Area: 49, White Area: 0, Reward: 0 ``` ```python state, reward, done, info = go_env.step(second_action) go_env.render('terminal') ``` ``` 0 1 2 3 4 5 6 ----------------------------- 0 | | | | | | | | ----------------------------- 1 | | | | | | | | ----------------------------- 2 | | | | | | B | | ----------------------------- 3 | | | | | | | | ----------------------------- 4 | | | | | | | | ----------------------------- 5 | | | W | | | | | ----------------------------- 6 | | | | | | | | ----------------------------- Turn: BLACK, Last Turn Passed: False, Game Over: False Black Area: 1, White Area: 1, Reward: 0 ``` ### High level API [GoEnv](gym_go/envs/go_env.py) defines the Gym environment for Go. It contains the highest level API for basic Go usage. ### Low level API [GoGame](gym_go/gogame.py) is the set of low-level functions that defines all the game logic of Go. `GoEnv`'s high level API is built on `GoGame`. These sets of functions are intended for a more detailed and finetuned usage of Go. # Scoring We use Trump Taylor scoring, a simple area scoring, to determine the winner. A player's _area_ is defined as the number of empty points a player's pieces surround plus the number of player's pieces on the board. The _winner_ is the player with the larger area (a game is tied if both players have an equal amount of area on the board). There is also support for `komi`, a bias score constant to balance the advantage of black going first. By default `komi` is set to 0. # Game ending A game ends when both players pass consecutively # Reward methods Reward methods are in _black_'s perspective * **Real**: * If game ended: * `-1` - White won * `0` - Game is tied * `1` - Black won * `0` - Otherwise * **Heuristic**: If the game is ongoing, the reward is `black area - white area`. If black won, the reward is `BOARD_SIZE**2`. If white won, the reward is `-BOARD_SIZE**2`. If tied, the reward is `0`. # State The `state` object that is returned by the `reset` and `step` functions of the environment is a `6 x BOARD_SIZE x BOARD_SIZE` numpy array. All values in the array are either `0` or `1` * **First and second channel:** represent the black and white pieces respectively. * **Third channel:** Indicator layer for whose turn it is * **Fourth channel:** Invalid moves (including ko-protection) for the next action * **Fifth channel:** Indicator layer for whether the previous move was a pass * **Sixth channel:** Indicator layer for whether the game is over # Action The `step` function takes in the action to execute and can be in the following forms: * a tuple/list of 2 integers representing the row and column or `None` for passing * a single integer representing the action in 1d space (i.e 9 would be (1,2) and 49 would be a pass for a 7x7 board)