Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle

TL;DR

This paper introduces a deep interactive reinforcement learning approach for autonomous underwater vehicle path following, combining human and environmental rewards to improve learning efficiency and adaptability in ocean exploration tasks.

Contribution

It proposes a novel deep interactive RL method that accelerates AUV learning and integrates human and environmental rewards for better adaptability.

Findings

Deep interactive RL speeds up AUV convergence compared to DQN.

Learning from both human and environmental rewards achieves comparable or better performance.

The methods are validated through simulations of straight line and sinusoidal path following.

Abstract

Autonomous underwater vehicle (AUV) plays an increasingly important role in ocean exploration. Existing AUVs are usually not fully autonomous and generally limited to pre-planning or pre-programming tasks. Reinforcement learning (RL) and deep reinforcement learning have been introduced into the AUV design and research to improve its autonomy. However, these methods are still difficult to apply directly to the actual AUV system because of the sparse rewards and low learning efficiency. In this paper, we proposed a deep interactive reinforcement learning method for path following of AUV by combining the advantages of deep reinforcement learning and interactive RL. In addition, since the human trainer cannot provide human rewards for AUV when it is running in the ocean and AUV needs to adapt to a changing environment, we further propose a deep reinforcement learning method that learns from both human rewards and environmental rewards at the same time. We test our methods in two path following tasks---straight line and sinusoids curve following of AUV by simulating in the Gazebo platform. Our experimental results show that with our proposed deep interactive RL method, AUV can converge faster than a DQN learner from only environmental reward. Moreover, AUV learning with our deep RL from both human and environmental rewards can also achieve a similar or even better performance than that with the deep interactive RL method and can adapt to the actual environment by further learning from environmental rewards.