An End-to-End Deep Reinforcement Learning-Based Intelligent Agent Capable of Autonomous Exploration in Unknown Environments

In recent years, machine learning (and as a result artificial intelligence) has experienced considerable progress. As a result, robots in different shapes and with different purposes have found their ways into our everyday life. These robots, which have been developed with the goal of human companionship, are here to help us in our everyday and routine life. These robots are different to the previous family of robots that were used in factories and static environments. These new robots are social robots that need to be able to adapt to our environment by themselves and to learn from their own experiences. In this paper, we contribute to the creation of robots with a high degree of autonomy, which is a must for social robots. We try to create an algorithm capable of autonomous exploration in and adaptation to unknown environments and implement it in a simulated robot. We go further than a simulation and implement our algorithm in a real robot, in which our sensor fusion method is able to overcome real-world noise and perform robust exploration.

[1]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[2]  Deok Jin Lee,et al.  Memory-based reinforcement learning algorithm for autonomous exploration in unknown environment , 2018 .

[3]  Sergey Levine,et al.  (CAD)$^2$RL: Real Single-Image Flight without a Single Real Image , 2016, Robotics: Science and Systems.

[4]  T. Arai,et al.  Optimization of obstacle avoidance using reinforcement learning , 2012, 2012 IEEE/SICE International Symposium on System Integration (SII).

[5]  Chih-Han Yu,et al.  Quadruped robot obstacle negotiation via reinforcement learning , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[6]  Tzuu-Hseng S. Li,et al.  Integrated particle swarm optimization algorithm based obstacle avoidance control design for home service robot , 2016, Comput. Electr. Eng..

[7]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[8]  Eduardo F. Morales,et al.  An Introduction to Reinforcement Learning , 2011 .

[9]  Sergey Levine,et al.  Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Michel Parent,et al.  Applying Evolutionary Optimisation to Robot Obstacle Avoidance , 2005, ArXiv.

[11]  Long-Ji Lin,et al.  Reinforcement learning for robots using neural networks , 1992 .

[12]  Nikolai Smolyanskiy,et al.  Toward low-flying autonomous MAV trail navigation using deep neural networks for environmental awareness , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[13]  Ming Liu,et al.  A deep-network solution towards model-less obstacle avoidance , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[14]  Weijia Zhou,et al.  Robot Obstacle Avoidance Learning Based on Mixture Models , 2016, J. Robotics.

[15]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[16]  Ming Liu,et al.  A robot exploration strategy based on Q-learning network , 2016, 2016 IEEE International Conference on Real-time Computing and Robotics (RCAR).

[17]  Rong Du,et al.  Path Planning with Obstacle Avoidance in PEGs: Ant Colony Optimization Method , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[18]  Peter Dayan,et al.  Q-learning , 1992, Machine Learning.

[19]  Dongkyoung Chwa,et al.  Obstacle Avoidance Method for Wheeled Mobile Robots Using Interval Type-2 Fuzzy Neural Network , 2015, IEEE Transactions on Fuzzy Systems.