  In real world settings, numerous constraints are present which are hard to
specify mathematically. However, for the real world deployment of reinforcement
learning (RL), it is critical that RL agents are aware of these constraints, so
that they can act safely. In this work, we consider the problem of learning
constraints from demonstrations of a constraint-abiding agent's behavior. We
experimentally validate our approach and show that our framework can
successfully learn the most likely constraints that the agent respects. We
further show that these learned constraints are \textit{transferable} to new
agents that may have different morphologies and/or reward functions. Previous
works in this regard have either mainly been restricted to tabular (discrete)
settings, specific types of constraints or assume the environment's transition
dynamics. In contrast, our framework is able to learn arbitrary
\textit{Markovian} constraints in high-dimensions in a completely model-free
setting. The code can be found it:
\url{https://github.com/shehryar-malik/icrl}.
