Daily Temperature Optimisation in Greenhouse by Reinforcement Learning

Ants, stochastic optimisation and reinforcement learning

Ant colonies are successful and resilient biological entities, which exhibit a number of desirable collective problem-solving behaviours. The study of ant colonies has recently inspired the development of artificial algorithms for stochastic optimisation and adaptive control, which attempt to mimic some of the properties of the biological counterpart. In this paper, we give a brief overview of ...

Alternating Optimisation and Quadrature for Robust Reinforcement Learning

Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems. However, the traditional approach for learning optimal policies in simulators does not utilise the opportunity to improve learning by adjusting certain environment variables – state features that are randomly determined by the environment in a physical setting but are controllable in a simulator...

Relating Ant Colony Optimisation and Reinforcement Learning Interim Report

Greenhouse Air Temperature Control Using the Particle Swarm Optimisation Algorithm

The particle swarm optimisation algorithm is proposed as a new method to design a model based predictive controller subject to restrictions. Its performance is compared with the one obtained by using a genetic algorithm for the environmental temperature control of a greenhouse. Controller outputs are computed in order to optimise future behaviour of the greenhouse environment, regarding set-poi...

Learning what to say and how to say it: Joint optimisation of spoken dialogue management and natural language generation

This paper argues that the problems of dialogue management (DM) and Natural Language Generation (NLG) in dialogue systems are closely related and can be fruitfully treated statistically, in a joint optimisation framework such as that provided by Reinforcement Learning (RL). We first review recent results and methods in automatic learning of dialogue management strategies for spoken and multimod...