Before revision:  We then train agents to maximize the given reward function subject to the learned constraints  . Previous works in this regard have either mainly been restricted to tabular settings or specific types of constraints or assume knowledge of transition dynamics of the environment .
After revision:  We further show that these learned constraints are . Previous works in this regard have either mainly been restricted to tabular settings or specific types of constraints or assume knowledge of transition dynamics of the environment .