Reproduce “Environment Design for Inverse Reinforcement Learning” on OpenResearch

About this paper

Thomas Kleine Buening, Victor Villin, Christos Dimitrakakis

Researchers at The Alan Turing Institute and Universit e de Neuch atel developed an adaptive environment design framework for Inverse Reinforcement Learning, enabling a learner to actively select environments that efficiently infer robust reward functions from expert demonstrations. This approach leads to more generalizable reward functions across varying dynamics and recovers more complete reward representations compared to passive or randomized observation methods.

Key insights

• Actively selecting environments to maximize Bayesian regret effectively forces experts to demonstrate behaviors that reveal previously unobserved or ambiguous aspects of the true reward function.
• Integrating demonstrations from multiple, distinct environmental dynamics allows for learning a more generalizable and robust reward function by distinguishing optimal behaviors under varying transition rules.
• Performance-based environment selection, rather than passive random sampling, is critical for sample-efficient reward inference in scenarios where expert demonstrations are costly.

Summary by alphaXiv.