# Combining Neural Networks and Tree Search for Task and Motion Planning in Challenging Environments

arXiv: https://arxiv.org/abs/1703.07887 · HTML: https://mlsanity.com/paper/1703.07887/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2017.

## Built on by (most cited first, 19 of 19)

- [Reward (Mis)design for Autonomous Driving](https://mlsanity.com/paper/2104.13906/) (2021)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [The Logical Options Framework](https://mlsanity.com/paper/2102.12571/) (2021)
- [Guided Imitation of Task and Motion Planning](https://mlsanity.com/paper/2112.03386/) (2021)
- [A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving](https://mlsanity.com/paper/2405.01440/) (2024)
- [Learning Efficient Abstract Planning Models that Choose What to Predict](https://mlsanity.com/paper/2208.07737/) (2022)
- [Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applications](https://mlsanity.com/paper/2303.08060/) (2023)
- [Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning](https://mlsanity.com/paper/2102.03119/) (2021)
- [Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning](https://mlsanity.com/paper/2102.03127/) (2021)
- [Using Common Random Numbers for Simulation-based Planning with Rollouts](https://mlsanity.com/paper/2605.04732/) (2026)
- [Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map](https://mlsanity.com/paper/2103.01039/) (2021)
- [Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning](https://mlsanity.com/paper/2110.00650/) (2021)
- [RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts](https://mlsanity.com/paper/2405.11139/) (2024)
- [Measurable Monte Carlo Search Error Bounds](https://mlsanity.com/paper/2106.04715/) (2021)
- [Assembly Planning from Observations under Physical Constraints](https://mlsanity.com/paper/2204.09616/) (2022)
- [A review of motion planning algorithms for intelligent robotics](https://mlsanity.com/paper/2102.02376/) (2021)
- [Spatially and Seamlessly Hierarchical Reinforcement Learning for State Space and Policy space in Autonomous Driving](https://mlsanity.com/paper/2111.05479/) (2021)
- [Manipulating Neural Path Planners via Slight Perturbations](https://mlsanity.com/paper/2403.18256/) (2024)
- [MCTS Based Dispatch of Autonomous Vehicles under Operational Constraints for Continuous Transportation](https://mlsanity.com/paper/2407.16200/) (2024)

## Abstract (arXiv)

We consider task and motion planning in complex dynamic environments for problems expressed in terms of a set of Linear Temporal Logic (LTL) constraints, and a reward function. We propose a methodology based on reinforcement learning that employs deep neural networks to learn low-level control policies as well as task-level option policies. A major challenge in this setting, both for neural network approaches and classical planning, is the need to explore future worlds of a complex and interactive environment. To this end, we integrate Monte Carlo Tree Search with hierarchical neural net control policies trained on expressive LTL specifications. This paper investigates the ability of neural networks to learn both LTL constraints and control policies in order to generate task plans in complex environments. We demonstrate our approach in a simulated autonomous driving setting, where a vehicle must drive down a road in traffic, avoid collisions, and navigate an intersection, all while obeying given rules of the road.