# Option Compatible Reward Inverse Reinforcement Learning

arXiv: https://arxiv.org/abs/1911.02723 · HTML: https://mlsanity.com/paper/1911.02723/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 7 Nov 2019.

## Placed in

- Offline RL and imitation learning from data / [Inverse reinforcement learning](https://mlsanity.com/offlinerl/inverse-reinforcement-learning/)

## Builds on (most cited first, 8 of 8)

- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [Unsupervised Perceptual Rewards for Imitation Learning](https://mlsanity.com/paper/1612.06699/) (2016)
- [Multi-Level Discovery of Deep Options](https://mlsanity.com/paper/1703.08294/) (2017)
- [OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1709.06683/) (2017)
- [HIRL: Hierarchical Inverse Reinforcement Learning for Long-Horizon Tasks with Delayed Rewards](https://mlsanity.com/paper/1604.06508/) (2016)
- [Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/1711.01503/) (2017)

## Built on by (most cited first, 2 of 2)

- [Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning](https://mlsanity.com/paper/2209.10974/) (2022)
- [Context-Hierarchy Inverse Reinforcement Learning](https://mlsanity.com/paper/2202.12597/) (2022)

## Abstract (arXiv)

Reinforcement learning in complex environments is a challenging problem. In particular, the success of reinforcement learning algorithms depends on a well-designed reward function. Inverse reinforcement learning (IRL) solves the problem of recovering reward functions from expert demonstrations. In this paper, we solve a hierarchical inverse reinforcement learning problem within the options framework, which allows us to utilize intrinsic motivation of the expert demonstrations. A gradient method for parametrized options is used to deduce a defining equation for the Q-feature space, which leads to a reward feature space. Using a second-order optimality condition for option parameters, an optimal reward function is selected. Experimental results in both discrete and continuous domains confirm that our recovered rewards provide a solution to the IRL problem using temporal abstraction, which in turn are effective in accelerating transfer learning tasks. We also show that our method is robust to noises contained in expert demonstrations.