Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk

• URL: http://arxiv.org/abs/2212.04123v1
• Date: Thu, 8 Dec 2022 07:46:42 GMT
• Title: Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk
• Authors: Fabian Hart, Ostap Okhrin
• Abstract summary: This paper proposes a general training environment where we gain control over the difficulty of the obstacle avoidance task. We found that shifting the training towards a greater task difficulty can massively increase the final performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the field of autonomous robots, reinforcement learning (RL) is an increasingly used method to solve the task of dynamic obstacle avoidance for mobile robots, autonomous ships, and drones. A common practice to train those agents is to use a training environment with random initialization of agent and obstacles. Such approaches might suffer from a low coverage of high-risk scenarios in training, leading to impaired final performance of obstacle avoidance. This paper proposes a general training environment where we gain control over the difficulty of the obstacle avoidance task by using short training episodes and assessing the difficulty by two metrics: The number of obstacles and a collision risk metric. We found that shifting the training towards a greater task difficulty can massively increase the final performance. A baseline agent, using a traditional training environment based on random initialization of agent and obstacles and longer training episodes, leads to a significantly weaker performance. To prove the generalizability of the proposed approach, we designed two realistic use cases: A mobile robot and a maritime ship under the threat of approaching obstacles. In both applications, the previous results can be confirmed, which emphasizes the general usability of the proposed approach, detached from a specific application context and independent of the agent's dynamics. We further added Gaussian noise to the sensor signals, resulting in only a marginal degradation of performance and thus indicating solid robustness of the trained agent.

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