# Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices

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## Abstract

**:**

## 1. Introduction

- We propose a new federated reinforcement learning scheme to allow multiple agents to control their own devices of the same type but with slightly different dynamics.
- We verify that the proposed scheme can expedite the learning process overall when the control policies are trained for the multiple devices.

## 2. Related Work

#### 2.1. Federated Reinforcement Learning

#### 2.2. Actor–Critic PPO

## 3. System Architecture & Overall Procedure

## 4. Federated Reinforcement Learning Algorithm

## 5. Experiments

Algorithm 1: Federated RL (Chief) |

Algorithm 2: Federated RL (Worker w) |

#### 5.1. Experiment Configuration

^{TM}-Servo 2 [26]). It is a highly unstable nonlinear IoT device and has been used as a usual device in the nonlinear control engineering field.

#### 5.2. State, Action, and Reward Formulation

#### 5.3. Effect of Gradient Sharing & Transfer Learning

^{TM}-Servo 2) 100 times in different directions, and measure the Pearson correlation between the changes of the motor and the pendulum angles for the three devices. Pearson correlation [33] is commonly used to find the relationship between two random variables. The Pearson correlation coefficient has $+1$ if the two variables X and Y are exactly the same, 0 if they are completely different, and $-1$ if they are exactly the same in the opposite direction. Table 2 shows the results of the homogeneity test for the dynamics of three RIP devices of the same type. As known from the two tables, the angles of motor and pendulum are changed differently even though the forces applied in different directions are constant over 100 times. For each RIP device, in particular, the change in motor angle is more varied than the change in the pendulum angle. For multiple RIP devices of the same type, as a result, their dynamics are slightly different from each other, even though they are produced on the same manufacturing line. This means that the additional learning at Worker I and III is still needed even after receiving the mature model of Worker II.

## 6. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 4.**Our experiment configuration with multiple rotary inverted pendulum (RIP) devices, multiple workers, and one chief.

**Figure 5.**Effectiveness of the proposed federated reinforcement learning scheme. The blue line represents the score for each round, and the red line represents the weighted moving average (WMA) of the scores from the last 10 rounds. The green dotted line indicates the loss value for each round. (

**a**) Change of score and loss values without the proposed scheme. (

**b**) Change of score and loss values with the proposed scheme.

Hyper-parameter | Value |
---|---|

Clipping parameter ($\u03f5$) | 0.9 |

Model optimization algorithm | Adam |

GAE parameter ($\lambda $) | 0.99 |

Learning rate for the critic model (${\eta}_{\mu}$) | 0.001 |

Learning rate for the actor model (${\eta}_{\theta}$) | 0.001 |

Trajectory memory size | 200 |

Batch size (U) | 64 |

Number of model optimizations in one round (K) | 10 |

(a) Pearson correlation matrix of motor angle changes | |||
---|---|---|---|

Motor Angle | RIP I | RIP II | RIP III |

RIP I | 1 | 0.77 | 0.86 |

RIP II | - | 1 | 0.75 |

RIP III | - | - | 1 |

(b) Pearson correlation matrix of pendulum angle changes | |||

Pendulum Angle | RIP I | RIP II | RIP III |

RIP I | 1 | 0.98 | 0.96 |

RIP II | - | 1 | 0.98 |

RIP III | - | - | 1 |

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**MDPI and ACS Style**

Lim, H.-K.; Kim, J.-B.; Heo, J.-S.; Han, Y.-H.
Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices. *Sensors* **2020**, *20*, 1359.
https://doi.org/10.3390/s20051359

**AMA Style**

Lim H-K, Kim J-B, Heo J-S, Han Y-H.
Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices. *Sensors*. 2020; 20(5):1359.
https://doi.org/10.3390/s20051359

**Chicago/Turabian Style**

Lim, Hyun-Kyo, Ju-Bong Kim, Joo-Seong Heo, and Youn-Hee Han.
2020. "Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices" *Sensors* 20, no. 5: 1359.
https://doi.org/10.3390/s20051359