# Automata Guided Reinforcement Learning With Demonstrations

arXiv: https://arxiv.org/abs/1809.06305 · HTML: https://mlsanity.com/paper/1809.06305/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [Multi-Agent Reinforcement Learning with Temporal Logic Specifications](https://mlsanity.com/paper/2102.00582/) (2021)
- [Learning from Demonstrations using Signal Temporal Logic](https://mlsanity.com/paper/2102.07730/) (2021)
- [Learning Performance Graphs from Demonstrations via Task-Based Evaluations](https://mlsanity.com/paper/2204.05909/) (2022)
- [Signal Temporal Logic-Guided Apprenticeship Learning](https://mlsanity.com/paper/2311.05084/) (2023)
- [Learning from Imperfect Demonstrations via Temporal Behavior Tree-Guided Trajectory Repair](https://mlsanity.com/paper/2604.04225/) (2026)

## Abstract (arXiv)

Tasks with complex temporal structures and long horizons pose a challenge for reinforcement learning agents due to the difficulty in specifying the tasks in terms of reward functions as well as large variances in the learning signals. We propose to address these problems by combining temporal logic (TL) with reinforcement learning from demonstrations. Our method automatically generates intrinsic rewards that align with the overall task goal given a TL task specification. The policy resulting from our framework has an interpretable and hierarchical structure. We validate the proposed method experimentally on a set of robotic manipulation tasks.