# Counter a Drone in a Complex Neighborhood Area by Deep Reinforcement Learning

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## Abstract

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## 1. Introduction

## 2. Methods

**Reinforcement learning**(RL) is an approach to AI based on trial and error experiences by interacting with the environment. In RL, the agent, or learner, can make a decision and take an action which updates the environment. Environment state is updated by each action that the agent takes. Also after each action the environment outputs a reward, in the form of a scalar value. Rewards reveal information about an action, whether that action results in positive or negative feedback. The objective of the agent is to maximize the cumulative reward. Each iteration between an action and the next action is known as step. An action can lead the environment to a terminal state, which is also known as the end of an episode. Thus, an episode is the set of steps starting at an initial state and finishing with a terminal state.

**transfer learning**(TL). The main purpose of TL is to improve the learning performance by using the experience from successfully pre-trained models [23].

## 3. DRL Model Definition

#### 3.1. Tools

#### 3.2. Model

#### 3.2.1. Environment

#### 3.2.2. State Representation

- Image

- Auxiliary Inputs

- The velocity of the agent in x and y directions: ${v}_{x}{v}_{y}$
- The distance from the agent to the goal in x and y directions and the Euclidean distance: ${d}_{x}{d}_{y}{d}_{t}$
- Track and the elevation angles between the agent and the goal: $\psi \zeta $
- The distances to the four geofence limits

#### 3.2.3. Agent’s Neural Network

#### 3.2.4. Actions

- Straight: Straight movement in direction of the heading with speed equal to 4 m/s
- Yaw left: Rotate clockwise around z axis with a 30 deg/s angular speed
- Yaw right: Rotate counter-clockwise around z axis with a 30 deg/s angular speed

#### 3.2.5. Rewards

## 4. Training Analysis & Results

#### 4.1. Training Setup

#### 4.2. Definition of Cases

#### 4.3. Training Results

#### 4.3.1. Case 1: Training at 30 m Height

- Baseline: Training including geofence and target drone

- Case 1.1: adding a stationary third drone

- Case 1.2: adding a stationary third drone and using pre-trained model from Baseline

- Case 1.3: adding movement to the third drone

- Case 1.4: adding movement to the third drone and using pre-trained model from Baseline

#### 4.3.2. Case 2: Training at Low Altitude, with Many Obstacles

- Case 2.1: without Transfer Learning

- Case 2.2: with Transfer Learning, using pre-trained model from Baseline

## 5. Further Model Results and Discussion

#### 5.1. Comparison of the Effects of Different Annealing Points in TL

#### 5.2. Comparison of the Explored Areas with or without TL

#### 5.3. Testing the Models at Low Altitude

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

UAV | Unmanned Aerial Vehicle |

AI | Artificial Intelligence |

DRL | Deep Reinforcement Learning |

CNN | Convolutional Neural Network |

RL | Reinforcement Learning |

TL | Transfer Learning |

NED | North East Down |

BVLOS | Beyond Visual Line of Sight |

ApI | Application Programming Interface |

## Appendix A. Neural Network Parameters

## References

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**Figure 1.**The agent-environment interaction in reinforcement learning [13].

**Figure 2.**Different metrics for measuring TL [23].

**Table 1.**Counter-drone techniques available according to [5].

Method Type | The Number of Cases Available |
---|---|

Jamming | 96 |

Net | 18 |

Spoofing | 12 |

Laser | 12 |

Machine Gun | 3 |

Electromagnetic Pulse | 2 |

Water Projector | 1 |

Sacrificial Collision Drone | 1 |

Other | 6 |

Reward | The Reason |
---|---|

+100 | Goal reached |

−100 | Collision: Obstacle (stationary or moving) or geofence |

−1 + $\Delta $ Distance + TrackAngle | Otherwise |

Case | Training | Steps | Annealing | Geofence | Obstacles |
---|---|---|---|---|---|

Baseline | FULL | 125K | 50K | YES | NONE |

Case 1.1 | FULL | 75K | 50K | YES | stationary 3rd drone |

Case 1.2 | Transferred | 50K | 25K | YES | stationary 3rd drone |

Case 1.3 | FULL | 75K | 50K | YES | non-stationary 3rd drone |

Case 1.4 | Transferred | 50K | 25K | YES | non-stationary 3rd drone |

Case 2.1 | FULL | 125K | 50K | YES | houses, trees, electrical, etc. |

Case 2.2 | Transferred | 50K | 10K | YES | houses, trees, electrical, etc. |

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Çetin, E.; Barrado, C.; Pastor, E.
Counter a Drone in a Complex Neighborhood Area by Deep Reinforcement Learning. *Sensors* **2020**, *20*, 2320.
https://doi.org/10.3390/s20082320

**AMA Style**

Çetin E, Barrado C, Pastor E.
Counter a Drone in a Complex Neighborhood Area by Deep Reinforcement Learning. *Sensors*. 2020; 20(8):2320.
https://doi.org/10.3390/s20082320

**Chicago/Turabian Style**

Çetin, Ender, Cristina Barrado, and Enric Pastor.
2020. "Counter a Drone in a Complex Neighborhood Area by Deep Reinforcement Learning" *Sensors* 20, no. 8: 2320.
https://doi.org/10.3390/s20082320