test environment
Amortized Active Causal Induction with Deep Reinforcement Learning
Our design policy successfully achieves amortized intervention design on the distribution of the training environment while also generalizing well to distribution shifts in test-time design environments.
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Object-CategoryAwareReinforcementLearning
Reinforcement Learning (RL) has achievedimpressiveprogress inrecent years, such asresults in Atari [24] and Go [28] in which RL agents even perform better than human beings.
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Deep reinforcement learning (RL) algorithms have demonstrated promising results on a variety of complex tasks, such as robotic manipulation [22, 13] and strategy games [27, 38].
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