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Grounded question answering-based curiosity formulation in CLEVR-Robot Environment. Code accompanying the paper "Ask & Explore: Grounded Question Answering for Curiosity-driven exploration."

Python 100.00%

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language-curiosity's Issues

how to set Sparse reward

in the paper, an example is “arrange the objects so that their colors range from blue to green in the horizontal direction, and keep the objects close vertically" what is its corresponding goal program?

the action in train_qa.py is a number but env.py need a tuple/list

in train_qa.py:
s, r, d, info = env.step(action, record_achieved_goal = True)
print(type(action)) <class 'numpy.int64'>

in clevr_rovot_env>env.py :
def step_continuous(self, a):
x, y, theta, r = a[0] * 0.7, a[1] * 0.7, a[2] * np.pi, a[3]
action needs a tuple/list

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