Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning

AbstractWe introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies’ performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to $$40\%$$ 40 % longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73% to 0.5%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute’s choice affects the aerial robot’s performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, Air Learning enables a broad class of deep RL research on UAVs. The source code is available at: https://github.com/harvard-edge/AirLearning.

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An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning

Applied Sciences ◽

10.3390/app11041514 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1514 ◽

Cited By ~ 2

Author(s):

Quang-Duy Tran ◽

Sang-Hoon Bae

Keyword(s):

Reinforcement Learning ◽

Traffic Congestion ◽

Autonomous Vehicles ◽

Penetration Rate ◽

Autonomous Vehicle ◽

Effective Means ◽

Urban Network ◽

Learning Agents ◽

Policy Optimization ◽

The Impact

To reduce the impact of congestion, it is necessary to improve our overall understanding of the influence of the autonomous vehicle. Recently, deep reinforcement learning has become an effective means of solving complex control tasks. Accordingly, we show an advanced deep reinforcement learning that investigates how the leading autonomous vehicles affect the urban network under a mixed-traffic environment. We also suggest a set of hyperparameters for achieving better performance. Firstly, we feed a set of hyperparameters into our deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle experiment in the urban network with different autonomous vehicle penetration rates. Thirdly, the advantage of leading autonomous vehicles is evaluated using entire manual vehicle and leading manual vehicle experiments. Finally, the proximal policy optimization with a clipped objective is compared to the proximal policy optimization with an adaptive Kullback–Leibler penalty to verify the superiority of the proposed hyperparameter. We demonstrate that full automation traffic increased the average speed 1.27 times greater compared with the entire manual vehicle experiment. Our proposed method becomes significantly more effective at a higher autonomous vehicle penetration rate. Furthermore, the leading autonomous vehicles could help to mitigate traffic congestion.

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Proximal policy optimization with model-based methods

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211935 ◽

2022 ◽

pp. 1-12

Author(s):

Shuailong Li ◽

Wei Zhang ◽

Huiwen Zhang ◽

Xin Zhang ◽

Yuquan Leng

Keyword(s):

Reinforcement Learning ◽

State Of The Art ◽

Transition Model ◽

Practical Applications ◽

Original Algorithm ◽

Policy Performance ◽

Model Based ◽

Model Free ◽

Future State ◽

Policy Optimization

Model-free reinforcement learning methods have successfully been applied to practical applications such as decision-making problems in Atari games. However, these methods have inherent shortcomings, such as a high variance and low sample efficiency. To improve the policy performance and sample efficiency of model-free reinforcement learning, we propose proximal policy optimization with model-based methods (PPOMM), a fusion method of both model-based and model-free reinforcement learning. PPOMM not only considers the information of past experience but also the prediction information of the future state. PPOMM adds the information of the next state to the objective function of the proximal policy optimization (PPO) algorithm through a model-based method. This method uses two components to optimize the policy: the error of PPO and the error of model-based reinforcement learning. We use the latter to optimize a latent transition model and predict the information of the next state. For most games, this method outperforms the state-of-the-art PPO algorithm when we evaluate across 49 Atari games in the Arcade Learning Environment (ALE). The experimental results show that PPOMM performs better or the same as the original algorithm in 33 games.

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A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers

2018 IEEE Intelligent Vehicles Symposium (IV) ◽

10.1109/ivs.2018.8500556 ◽

2018 ◽

Cited By ~ 26

Author(s):

Pin Wang ◽

Ching-Yao Chan ◽

Arnaud de La Fortelle

Keyword(s):

Reinforcement Learning ◽

Lane Change

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Model-based Ensemble Reinforcement Learning with Soft Proximal Policy Optimization

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) ◽

10.1109/ddcls52934.2021.9455658 ◽

2021 ◽

Author(s):

Dazi Li ◽

Fuqiang Zhu

Keyword(s):

Reinforcement Learning ◽

Model Based ◽

Policy Optimization

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Research on the Multiagent Joint Proximal Policy Optimization Algorithm Controlling Cooperative Fixed-Wing UAV Obstacle Avoidance

Sensors ◽

10.3390/s20164546 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4546

Author(s):

Weiwei Zhao ◽

Hairong Chu ◽

Xikui Miao ◽

Lihong Guo ◽

Honghai Shen ◽

...

Keyword(s):

Reinforcement Learning ◽

Attitude Control ◽

Cooperative Control ◽

Learning Algorithm ◽

State Equations ◽

Learning Agent ◽

Environmental Adaptability ◽

Decentralized Execution ◽

Policy Optimization ◽

Multi Uav

Multiple unmanned aerial vehicle (UAV) collaboration has great potential. To increase the intelligence and environmental adaptability of multi-UAV control, we study the application of deep reinforcement learning algorithms in the field of multi-UAV cooperative control. Aiming at the problem of a non-stationary environment caused by the change of learning agent strategy in reinforcement learning in a multi-agent environment, the paper presents an improved multiagent reinforcement learning algorithm—the multiagent joint proximal policy optimization (MAJPPO) algorithm with the centralized learning and decentralized execution. This algorithm uses the moving window averaging method to make each agent obtain a centralized state value function, so that the agents can achieve better collaboration. The improved algorithm enhances the collaboration and increases the sum of reward values obtained by the multiagent system. To evaluate the performance of the algorithm, we use the MAJPPO algorithm to complete the task of multi-UAV formation and the crossing of multiple-obstacle environments. To simplify the control complexity of the UAV, we use the six-degree of freedom and 12-state equations of the dynamics model of the UAV with an attitude control loop. The experimental results show that the MAJPPO algorithm has better performance and better environmental adaptability.

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