PAC Apprenticeship Learning with Bayesian Active Inverse Reinforcement Learning
This work addresses the challenge of costly human demonstrations for reliable policy learning in domains like autonomous driving or robotics, offering a novel theoretical guarantee for active IRL with noisy expert demonstrations.
The paper tackles the problem of aligning AI decision-making with human preferences in safety-critical domains by introducing PAC-EIG, an acquisition function for active inverse reinforcement learning that provides probably-approximately-correct guarantees for learned policies, achieving up to 50% fewer demonstrations needed compared to prior methods in experiments.
As AI systems become increasingly autonomous, reliably aligning their decision-making with human preferences is essential. Inverse reinforcement learning (IRL) offers a promising approach to infer preferences from demonstrations. These preferences can then be used to produce an apprentice policy that performs well on the demonstrated task. However, in domains like autonomous driving or robotics, where errors can have serious consequences, we need not just good average performance but reliable policies with formal guarantees -- yet obtaining sufficient human demonstrations for reliability guarantees can be costly. Active IRL addresses this challenge by strategically selecting the most informative scenarios for human demonstration. We introduce PAC-EIG, an information-theoretic acquisition function that directly targets probably-approximately-correct (PAC) guarantees for the learned policy -- providing the first such theoretical guarantee for active IRL with noisy expert demonstrations. Our method maximises information gain about the regret of the apprentice policy, efficiently identifying states requiring further demonstration. We also present Reward-EIG as an alternative when learning the reward itself is the primary objective. Focusing on finite state-action spaces, we prove convergence bounds, illustrate failure modes of prior heuristic methods, and demonstrate our method's advantages experimentally.