Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching

TL;DR

This paper introduces a non-adversarial inverse reinforcement learning method that uses successor feature matching and policy gradient optimization, enabling efficient learning from minimal demonstrations without reward modeling.

Contribution

The authors propose a novel IRL approach that avoids adversarial training by directly optimizing policies through successor features, applicable even in state-only scenarios.

Findings

Learns effectively from a single demonstration

Outperforms existing methods on control tasks

Operates without reward function learning

Abstract

In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Traditionally, IRL is treated as an adversarial game, where an adversary searches over reward models, and a learner optimizes the reward through repeated RL procedures. This game-solving approach is both computationally expensive and difficult to stabilize. In this work, we propose a novel approach to IRL by direct policy optimization: exploiting a linear factorization of the return as the inner product of successor features and a reward vector, we design an IRL algorithm by policy gradient descent on the gap between the learner and expert features. Our non-adversarial method does not require learning a reward function and can be solved seamlessly with existing actor-critic RL algorithms. Remarkably, our approach works in state-only settings without expert action labels, a setting which behavior cloning (BC) cannot solve. Empirical results demonstrate that our method learns from as few as a single expert demonstration and achieves improved performance on various control tasks.