Evolutionary Swarm Robotics using Epigenetics Learning in Dynamic Environment

Intelligent robots have been widely studied and investigated to replace, fulfilling a complex mission in a hazardous environment. Lately, swarm robotics, a group of collaborative robots, has become popular because it offers benefits over a single intelligent system. Many strategies have been developed to achieve collective and decentralised control applying evolutionary algorithms. However, since the evolutionary algorithm relies principally on an individual fitness function to explore the solution space, achieving swarm robotics' collaborative behaviour in a dynamic environment becomes a problem. This is due to the lack of adaptation in most of the evolutionary methods. In order to thrive in such environment, external stimuli and rewards from the environment should be utilised as ``knowledge'' to achieve the intelligent behaviour currently lacking in evolutionary swarm robotics. The aims of this research are: (1) to develop novel reward-based evolutionary swarm learning using mechanisms of epigenetic inheritance; and (2) to identify an efficient learning method for the epigenetic layer achieving a decision-making strategy in a dynamic environment. This research's contributions are the development of reward-based co-learning algorithm and co-evolution using epigenetic-based knowledge backup. The reward-based co-learning algorithm enables the swarm to obtain knowledge of the dynamic environment and override the objective-based function to evaluate internal and external problems. An advantage of this is that the learning mechanism also enables the swarm to explore potentially better behaviour without the constraint of an ill-defined objective function. Simulated search-and-rescue missions using a swarm of UAVs shows that individual behaviour evolves differently although each member has the same physical characteristics and the same set of actions. As an addition to reward-based multi-agent learning mechanisms, epigenetics is introduced as a decision-making layer. The epigenetic layer has two functions: there are genetic regulators, as well as an epigenetic inheritance (the epigenetic mechanism). The first is the function of an epigenetic layer regulating how genetic information is expressed as agent’s behaviour (the ``phenotype''). Thus, utilising the regulatory function, the agent is able to switch genetic strategy or decision-making based on external stimulus from the aforementioned reward-based learning. The second function is that epigenetic inheritance enables sharing of genetic regulation and decision-making layer between agents. In summary, this research extends the current literature on evolutionary swarm robotics and decentralised multi-agent learning mechanisms. The combination of both advances the decentralised mechanism in obtaining information and improve collective behaviour