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keras_gym_env.py
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keras_gym_env.py
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# File Source: https://keras-gym.readthedocs.io/en/stable/_modules/keras_gym/envs/self_play.html#ConnectFourEnv
from gym import Env
from gym.spaces import Discrete, MultiDiscrete
import numpy as np
#from ..base.errors import UnavailableActionError, EpisodeDoneError
# next three classes source: https://github.com/KristianHolsheimer/keras-gym/blob/12d83ec9de644cd25b0a7ed4fe250850f6b998ea/keras_gym/base/errors.py
class KerasGymError(Exception):
pass
class EpisodeDoneError(KerasGymError):
pass
class UnavailableActionError(KerasGymError):
pass
__all__ = (
'ConnectFourEnv',
)
class ConnectFourEnv(Env):
"""
An adversarial environment for playing the `Connect-Four game
<https://en.wikipedia.org/wiki/Connect_Four>`_.
Attributes
----------
action_space : gym.spaces.Discrete(7)
The action space.
observation_space : MultiDiscrete(nvec)
The state observation space, representing the position of the current
player's tokens (``s[1:,:,0]``) and the other player's tokens
(``s[1:,:,1]``) as well as a mask over the space of actions, indicating
which actions are available to the current player (``s[0,:,0]``) or the
other player (``s[0,:,1]``).
**Note:** The "current" player is relative to whose turn it is, which
means that the entries ``s[:,:,0]`` and ``s[:,:,1]`` swap between
turns.
max_time_steps : int
Maximum number of timesteps within each episode.
available_actions : array of int
Array of available actions. This list shrinks when columns saturate.
win_reward : 1.0
The reward associated with a win.
loss_reward : -1.0
The reward associated with a loss.
draw_reward : 0.0
The reward associated with a draw.
move_reward : 0
The reward associated with a move.
wrong_move_reward : -0.5
The reward associated with an unavailable move.
""" # noqa: E501
# class attributes
num_rows = 6
num_cols = 7
num_players = 2
win_reward = 1.0
loss_reward = -win_reward
draw_reward = 0.0
move_reward = 0
wrong_move_reward = -0.5
action_space = Discrete(num_cols)
observation_space = MultiDiscrete(
nvec=np.full((num_rows + 1, num_cols, num_players), 2, dtype='uint8'))
max_time_steps = int(num_rows * num_cols)
filters = np.array([
[[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 1, 1, 1]],
[[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 1, 1, 1],
[0, 0, 0, 0]],
[[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]],
[[0, 0, 0, 1],
[0, 0, 1, 0],
[0, 1, 0, 0],
[1, 0, 0, 0]],
[[0, 0, 0, 0],
[1, 1, 1, 1],
[0, 0, 0, 0],
[0, 0, 0, 0]],
[[1, 1, 1, 1],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]],
[[1, 0, 0, 0],
[1, 0, 0, 0],
[1, 0, 0, 0],
[1, 0, 0, 0]],
[[0, 1, 0, 0],
[0, 1, 0, 0],
[0, 1, 0, 0],
[0, 1, 0, 0]],
[[0, 0, 1, 0],
[0, 0, 1, 0],
[0, 0, 1, 0],
[0, 0, 1, 0]],
[[0, 0, 0, 1],
[0, 0, 0, 1],
[0, 0, 0, 1],
[0, 0, 0, 1]],
], dtype='uint8')
def __init__(self):
self._init_state()
def reset(self):
"""
Reset the environment to the starting position.
Returns
-------
s : 3d-array, shape: [num_rows + 1, num_cols, num_players]
A state observation, representing the position of the current
player's tokens (``s[1:,:,0]``) and the other player's tokens
(``s[1:,:,1]``) as well as a mask over the space of actions,
indicating which actions are available to the current player
(``s[0,:,0]``) or the other player (``s[0,:,1]``).
**Note:** The "current" player is relative to whose turn it is,
which means that the entries ``s[:,:,0]`` and ``s[:,:,1]`` swap
between turns.
"""
self._init_state()
return self.state
def step(self, a, return_wrong : bool = False):
"""
Take one step in the MDP, following the single-player convention from
gym.
Parameters
----------
a : int, options: {0, 1, 2, 3, 4, 5, 6}
The action to be taken. The action is the zero-based count of the
possible insertion slots, starting from the left of the board.
Returns
-------
s_next : array, shape [6, 7, 2]
A next-state observation, representing the position of the current
player's tokens (``s[1:,:,0]``) and the other player's tokens
(``s[1:,:,1]``) as well as a mask over the space of actions,
indicating which actions are available to the current player
(``s[0,:,0]``) or the other player (``s[0,:,1]``).
**Note:** The "current" player is relative to whose turn it is,
which means that the entries ``s[:,:,0]`` and ``s[:,:,1]`` swap
between turns.
r : float
Reward associated with the transition
:math:`(s, a)\\to s_\\text{next}`.
**Note:** Since "current" player is relative to whose turn it is,
you need to be careful about aligning the rewards with the correct
state or state-action pair. In particular, this reward :math:`r` is
the one associated with the :math:`s` and :math:`a`, i.e. *not*
aligned with :math:`s_\\text{next}`.
done : bool
Whether the episode is done.
info : dict or None
A dict with some extra information (or None).
"""
if self.done:
raise EpisodeDoneError("please reset env to start new episode")
if not self.action_space.contains(a):
raise ValueError("invalid action")
if a not in self.available_actions:
if return_wrong:
# the last return value now is the wrong bool
return self.state, self.wrong_move_reward, self.done , True #, {'state_id': self.state_id}
else:
raise UnavailableActionError("action is not available")
# swap players
self._players = np.roll(self._players, -1)
# update state
self._state[self._levels[a], a] = self._players[0]
self._prev_action = a
# run logic
self.done, reward = self._done_reward(a)
return (self.state, reward, self.done, False) if return_wrong else (self.state, reward, self.done)
def render(self, *args, **kwargs):
"""
Render the current state of the environment.
"""
# lookup for symbols
symbol = {
1: u'\u25CF', # player 1 token (agent)
2: u'\u25CB', # player 2 token (adversary)
-1: u'\u25BD', # indicator for player 1's last action
-2: u'\u25BC', # indicator for player 2's last action
}
# render board
hrule = '+---' * self.num_cols + '+\n'
board = " "
board += " ".join(
# symbol.get(-(a == self._prev_action) * self._players[1], " ")
symbol.get( not (a == self._prev_action) * self._players[1], " ")
for a in range(self.num_cols))
board += " \n"
board += hrule
for i in range(self.num_rows):
board += "| "
board += " | ".join(
symbol.get(self._state[i, j], " ")
for j in range(self.num_cols))
board += " |\n"
board += hrule
board += " 0 1 2 3 4 5 6 \n" # actions
print(board)
@property
def state(self):
stacked_layers = np.stack((
(self._state == self._players[0]).astype('uint8'),
(self._state == self._players[1]).astype('uint8'),
), axis=-1) # shape: [num_rows, num_cols, num_players]
available_actions_mask = np.zeros(
(1, self.num_cols, self.num_players), dtype='uint8')
available_actions_mask[0, self.available_actions, :] = 1
return np.concatenate((available_actions_mask, stacked_layers), axis=0)
@property
def state_id(self):
p = str(self._players[0])
d = '1' if self.done else '0'
if self._prev_action is None:
a = str(self.num_cols)
else:
a = str(self._prev_action)
s = ''.join(self._state.ravel().astype('str')) # base-3 string
s = '{:017x}'.format(int(s, 3)) # 17-char hex string
return p + d + a + s # 20-char hex string
def set_state(self, state_id):
# decode state id
p = int(state_id[0], 16)
d = int(state_id[1], 16)
a = int(state_id[2], 16)
assert p in (1, 2)
assert d in (0, 1)
assert self.action_space.contains(a) or a == self.num_cols
self._players[0] = p # 1 or 2
self._players[1] = 3 - p # 2 or 1
self.done = d == 1
self._prev_action = None if a == self.num_cols else a
s = np.base_repr(int(state_id[3:], 16), 3)
z = np.zeros(self.num_rows * self.num_cols, dtype='uint8')
z[-len(s):] = np.array(list(s), dtype='uint8')
self._state = z.reshape((self.num_rows, self.num_cols))
self._levels = np.full(self.num_cols, self.num_rows - 1, dtype='uint8')
for j in range(self.num_cols):
for i in self._state[::-1, j]:
if i == 0:
break
self._levels[j] -= 1
@property
def available_actions(self):
actions = np.argwhere(
(self._levels >= 0) & (self._levels < self.num_rows)).ravel()
assert actions.size <= self.num_cols
return actions
@property
def available_actions_mask(self):
mask = np.zeros(self.num_cols, dtype='bool')
mask[self.available_actions] = True
return mask
def _init_state(self):
self._prev_action = None
self._players = np.array([1, 2], dtype='uint8')
self._state = np.zeros((self.num_rows, self.num_cols), dtype='uint8')
self._levels = np.full(self.num_cols, self.num_rows - 1, dtype='uint8')
self.done = False
def _done_reward(self, a):
"""
Check whether the last action `a` by the current player resulted in a
win or draw for player 1 (the agent). This contains the main logic and
implements the rules of the game.
"""
assert self.action_space.contains(a)
# update filling levels
self._levels[a] -= 1
s = self._state == self._players[0]
for i0 in range(2, -1, -1):
i1 = i0 + 4
for j0 in range(4):
j1 = j0 + 4
if np.any(np.tensordot(self.filters, s[i0:i1, j0:j1]) == 4):
return True, self.win_reward
# check for a draw
if len(self.available_actions) == 0:
return True, self.draw_reward
# this is what's returned throughout the episode
return False, self.wrong_move_reward