{"product_id":"learning-a-fuzzy-control-with-an-adaptive-representation","title":"Learning a Fuzzy Control with an Adaptive Representation","description":"\u003cp\u003eProgramming an autonomous robot to operate in a real-world environment is extremely challenging. Machine learning techniques are seen as a promising alternative to the reliance on building accurate models of the robot platform and its environment by learning a controller while on the robot platform in the real environment. Currently, the majority of machine learning techniques applied to learning a robot controller use a uniform or pre-defined internal representation provided by a human designer. A uniform representation typically provides poor generalisation for control applications, and a pre-defined representation requires the designer to have an in-depth knowledge of the desired control policy. In this thesis, the approach taken is to reduce the reliance on a human designer by adapting the internal representation, to improve the generalisation over the control policy, during the learning process.\u003c\/p\u003e","brand":"BooksWholesale","offers":[{"title":"Hardcover","offer_id":44347872936097,"sku":"9781447810001","price":52.56,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0462\/1120\/3233\/files\/1wyr488-front-shortedge-384.jpg?v=1716170626","url":"https:\/\/bookswholesale.myshopify.com\/products\/learning-a-fuzzy-control-with-an-adaptive-representation","provider":"BooksWholesale","version":"1.0","type":"link"}