RLforUTracking

Deep Reinforcement Learning methods for Underwater target Tracking This is a set of tools developed to train an agent (and multiple agents) to find the optimal path to localize and track a target (and multiple targets). he deep Reinforcement Learning (RL) algorithms implemented are: Deep Deterministic Policy Gradient (DDPG) Twin-Delayed DDPG (TD3) Soft Actor-Critic (SAC) The environment to train the agents is based on the OpenAI Particle. The main objective is to find the optimal path that an autonomous vehicle (e.g. autonomous underwater vehicles (AUV) or autonomous surface vehicles (ASV)) should follow in order to localize and track an underwater target using range-only and single-beacon algorithms. The target estimation algorithms implemented are based on: Least Squares (LS) Particle Filter (PF) More information at this Github repository: https://github.com/imasmitja/RLforUTrackin