Created
September 14, 2022 17:28
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LunarLander-v2 using image
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import gym | |
import numpy as np | |
from gym.spaces import Box | |
class ValidateLunarLanderImageEnv(gym.Env): | |
def __init__(self): | |
self.env = gym.make('LunarLander-v2') | |
obs = self.reset() | |
self.observation_space = Box( | |
low=0, | |
high=255, | |
dtype=np.uint8, | |
shape=obs.shape) | |
self.action_space = self.env.action_space | |
def convert_obs(self, observation): | |
observation = np.atleast_3d(observation) | |
if observation.max().item() != 0.: | |
observation *= 255.0/observation.max() | |
return observation.astype(np.uint8) | |
def reset(self): | |
observation = self.env.reset() | |
return self.convert_obs(observation) | |
def step(self, action: int): | |
obs, reward, done, info = self.env.step(action) | |
obs = self.convert_obs(obs) | |
return obs, reward, done, info | |
def close(self): | |
self.env.close() | |
return self.close() |
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Hi @ipsec ... my comment is unrelated to this file. I'm trying to get your solution to this stochastic muzero nan training problem which was closed on another repo. Can you please share your solution? Thank you very much
DHDev0/Stochastic-muzero#2 (comment)