In this project, we explored an end-to-end learning approach to train a navigation agent from raw perception information (i.e. laser scans) to velocity commands. Specifically, we consider two off-policy learning algorithms, Deep Q Network and Deep Deterministic Policy Gradient and train agents in different simulated training environments to perform point-to-point (P2P) navigation without colliding with obstacles. We evaluate our models against a baseline, Move-Base, which is a well-known classical navigation implementation in ROS. This report discusses our implementation, simulation results, findings and lessons learned
Team : Abhishek Jain, Kavit Nilesh Shah, Kenechukwu C. Mbanisi, Sanjeev Kannan
Separate branches created for DQN and DDPG implementations.