{'fraction_non_alphanumeric': 0.016519823788546256, 'fraction_numerical': 0.0, 'mean_word_length': 5.7835820895522385, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A bootstrapping method for autonomous and in site learning of generic navigation behaviour

To understand the behaviour of natural autonomous systems, research is carried out on artificial autonomous agents. The paper focuses on how simple behaviours can be learnt autonomously using a bootstrapping method. Firstly, a two dimensional self-organising map is realised which provides the agent's sense of orientation. Once this relative positioning system has been established, the agent learns to navigate towards a target using the reinforcement learning technique of Q-learning. Since only neural network processing is used, this technique emulates the distributed and adaptive information processing found in natural autonomous systems. Furthermore, due to its generality, the neural implementation developed is transferable to other artificial autonomous agents with different sensors and effector suites.