[英]Open AI Gym Observation Space shape Problem
Im trying to solve the Yatzee game once and forever using reinforcement learning.我正在尝试使用强化学习一劳永逸地解决 Yatzee 游戏。 Sadly when i check the gyms conformity with stable baselines, it is critisizing the shape of my observation space.
可悲的是,当我检查健身房是否符合稳定的基线时,它批评了我观察空间的形状。 So ive put a print statement in the constructor thats telling me the shape of my observation space, as soon as i create an object.
因此,我在创建 object 后立即在构造函数中放置了一条打印语句,告诉我观察空间的形状。
class YatzeeEnv{
game_state = np.zeros(19, np.int32)
def __init__(self):
self.action_space = gym.spaces.Discrete(19)
self.observation_space = gym.spaces.MultiDiscrete(19)
for x in self.game_state_adresses:
self.game_state[x] = -1
self.reroll()
self.game_state[self.reroll_state] = 0
print(self.game_state.shape)
print(self.observation_space.shape)
}
a = YatzeeEnv()
Sadly the output of this is可悲的是,这是 output
np array shape: (19,)
Observation space shape: ()
Why is this?为什么是这样? I thought
gym.spaces.MultiDiscrete(19)
defines the observation space as int array with 19 values.我认为
gym.spaces.MultiDiscrete(19)
将观察空间定义为具有 19 个值的 int 数组。
From the docs...从文档...
This represents the cartesian product of arbitrary :class:`Discrete` spaces.
It is useful to represent game controllers or keyboards where each key can be represented as a discrete action space.
Note:
Some environment wrappers assume a value of 0 always represents the NOOP action.
e.g. Nintendo Game Controller - Can be conceptualized as 3 discrete action spaces:
1. Arrow Keys: Discrete 5 - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4] - params: min: 0, max: 4
2. Button A: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1
3. Button B: Discrete 2 - NOOP[0], Pressed[1] - params: min: 0, max: 1
It can be initialized as ``MultiDiscrete([ 5, 2, 2 ])`` such that a sample might be ``array([3, 1, 0])``.
Although this feature is rarely used, :class:`MultiDiscrete` spaces may also have several axes
if ``nvec`` has several axes:
Example::
>> d = MultiDiscrete(np.array([[1, 2], [3, 4]]))
>> d.sample()
array([[0, 0],
[2, 3]])
If you have only one action space, you dont have to use MultiDiscrete.如果您只有一个动作空间,则不必使用 MultiDiscrete。 Or use MultiDiscrete([19]).
或者使用 MultiDiscrete([19])。
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