Benchmarking Constraint Inference in Inverse Reinforcement Learning

Understand

When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints.

• In many real-world problems, however, the constraints are often hard to specify mathematically and unknown to the RL agents.
• To tackle these issues, Inverse Constrained Reinforcement Learning (ICRL) empirically estimates constraints from expert demonstrations.
• As an emerging research topic, ICRL does not have common benchmarks, and previous works tested algorithms under hand-crafted environments with manually-generated expert demonstrations.