 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 .
<meaning-changed>  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 .
 We then train agents to maximize the given reward function subject to the learned constraints to new agents that may have different morphologies and/or reward functions . 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 .