mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-23 23:12:46 +00:00
aff7a643cc
* Doom - Added reward_threshold and timestep_limit for all environments * Doom - Returning all available game variables * Doom - Moved _seed to doom_env to avoid repetition in every environment * Doom - Added ALT_ATTACK and made all action_space equivalent (same controls between environments). * Doom - Actions can either be a short list of allowed actions or the full list of 41 commands * Doom - Returning black observation space on error or is_finished, rather than empty list (which was triggering an error) * Doom - HighLow.sample() returns the small list. * Doom - Updated difficulty for some missions * Doom - Fixed inconsistency between controls.md and deathmatch.cfg * Doom - Issue #168 - Remove sleep statement from DoomEnv render * Doom - Only using full action space (43 keys) - Added 'normal', 'fast' and 'human' mode - Set non-deterministic to True - Set video.frames_per_second to 35 - Properly returning game variables * Replaced warnings.warn by logger.warn * Doom - Added NUM_ACTIONS and action_idx instead of x * Doom - Added NUM_ACTIONS and action_idx instead of x * Doom - reset() only calls game.new_episode() after first call * Doom is now deterministic * Doom - Partial fix for issue #167 - DoomDeathmatch environment crashes sporadically * Doom - Standardized envs, simplified _reset * Doom - Removed temporary fix for issue #167 * Doom - Added scoreboard summary and description
62 lines
2.4 KiB
Python
62 lines
2.4 KiB
Python
import numpy as np
|
|
|
|
import gym
|
|
from gym.spaces import prng
|
|
|
|
class HighLow(gym.Space):
|
|
"""
|
|
A matrix of dimensions n x 3, where
|
|
|
|
- n is the number of options in the space (e.g. buttons that can be pressed simultaneously)
|
|
- u[0] (the first column) is the minimum value (inclusive) that the option can have
|
|
- u[1] (the second column) is the maximum value (inclusive) that the option can have
|
|
- u[2] (the third column) is the precision (0 = rounded to integer, 2 = rounded to 2 decimals)
|
|
|
|
e.g. if the space is composed of ATTACK (values: 0-100), MOVE_LEFT(0-1), MOVE_RIGHT(0,1)
|
|
the space would be [ [0.0, 100.0, 2], [0, 1, 0], [0, 1, 0] ]
|
|
"""
|
|
def __init__(self, matrix):
|
|
"""
|
|
A matrix of shape (n, 3), where the first column is the minimum (inclusive), the second column
|
|
is the maximum (inclusive), and the third column is the precision (number of decimals to keep)
|
|
|
|
e.g. np.matrix([[0, 1, 0], [0, 1, 0], [0.0, 100.0, 2]])
|
|
"""
|
|
(num_rows, num_cols) = matrix.shape
|
|
assert num_rows >= 1
|
|
assert num_cols == 3
|
|
self.matrix = matrix
|
|
self.num_rows = num_rows
|
|
|
|
def sample(self):
|
|
# For each row: round(random .* (max - min) + min, precision)
|
|
max_minus_min = np.zeros(shape=(self.matrix.shape[0], 1), dtype=np.int32)
|
|
for i in range(self.matrix.shape[0]): # Must use this conversion to avoid overflows
|
|
max_minus_min[i] = int(self.matrix[i, 1]) - int(self.matrix[i, 0])
|
|
random_matrix = np.multiply(max_minus_min, prng.np_random.rand(self.num_rows, 1)) + self.matrix[:, 0]
|
|
rounded_matrix = np.zeros(self.num_rows, dtype=np.int32)
|
|
for i in range(self.num_rows):
|
|
rounded_matrix[i] = round(random_matrix[i, 0], int(self.matrix[i, 2]))
|
|
return rounded_matrix.tolist()
|
|
|
|
def contains(self, x):
|
|
if len(x) != self.num_rows:
|
|
return False
|
|
for i in range(self.num_rows):
|
|
if not (self.matrix[i, 0] <= x[i] <= self.matrix[i, 1]):
|
|
return False
|
|
return True
|
|
|
|
def to_jsonable(self, sample_n):
|
|
return np.array(sample_n).tolist()
|
|
def from_jsonable(self, sample_n):
|
|
return [np.asarray(sample) for sample in sample_n]
|
|
|
|
@property
|
|
def shape(self):
|
|
return self.matrix.shape[0]
|
|
def __repr__(self):
|
|
return "High-Low" + str(self.shape)
|
|
def __eq__(self, other):
|
|
return self.matrix == other.matrix
|