# Deep Recurrent Q-learning for Energy-constrained Coverage with a Mobile Robot

arXiv: https://arxiv.org/abs/2210.00327 · HTML: https://mlsanity.com/paper/2210.00327/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 1 Oct 2022.

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

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Revisiting Fundamentals of Experience Replay](https://mlsanity.com/paper/2007.06700/) (2020)
- [UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning](https://mlsanity.com/paper/2003.02609/) (2020)
- [A Survey of Shortest-Path Algorithms](https://mlsanity.com/paper/1705.02044/) (2017)
- [Reinforcement Learning-Based Coverage Path Planning with Implicit Cellular Decomposition](https://mlsanity.com/paper/2110.09018/) (2021)
- [Algorithms for Routing an Unmanned Aerial Vehicle in the presence of Refueling Depots](https://mlsanity.com/paper/1304.0494/) (2013)

## Built on by (most cited first, 1 of 1)

- [Deep Reinforcement Learning for Dynamic Battery Management of Autonomous Order Pickers](https://mlsanity.com/paper/2607.05683/) (2026)

## Abstract (arXiv)

In this paper, we study the problem of coverage of an environment with an energy-constrained robot in the presence of multiple charging stations. As the robot's on-board power supply is limited, it might not have enough energy to cover all the points in the environment with a single charge. Instead, it will need to stop at one or more charging stations to recharge its battery intermittently. The robot cannot violate the energy constraint, i.e., visit a location with negative available energy. To solve this problem, we propose a deep Q-learning framework that produces a policy to maximize the coverage and minimize the budget violations. Our proposed framework also leverages the memory of a recurrent neural network (RNN) to better suit this multi-objective optimization problem. We have tested the presented framework within a 16 x 16 grid environment having charging stations and various obstacle configurations. Results show that our proposed method finds feasible solutions and outperforms a comparable existing technique.