This paper presents a simple yet general framework for employing deep architectures to solve the inverse reinforcement learning (IRL) problem. In particular, we propose to exploit the representational capacity and favourable computational complexity of deep networks to approximate complex, nonlinear reward functions in scenarios with large state spaces. This leads to a framework with the ability to make reward predictions in constant time rather than scaling cubically in the number of state rewards observed. Furthermore, we show that the maximum entropy paradigm for IRL lends itself naturally to the efficient training of deep architectures. The approach presented outperforms the state-of-the-art on a new benchmark with a complex underlying reward structure representing strong interactions between features while exhibiting performance commensurate to state-of-the-art methods on a number of established benchmarks of comparatively low complexity.
REF uses a multi-layer neural network to represent nonlinear reward functions.
Deep Inverse Reinforcement Learning.
ArXiv
ArXiv
Computer Science