The design of effective, robust and autonomous controllers for multi-agent and multi-robot system... more The design of effective, robust and autonomous controllers for multi-agent and multi-robot systems is a long-standing problem in the fields of computational intelligence and robotics. Whilst nature-inspired problem-solving techniques such as reinforcement learning (RL) and evolutionary algorithms (EA) are often used to adapt controllers for solving such tasks, the complexity of such tasks increases with the addition of more agents (or robots) in difficult environments. This is due to specific issues related to task complexity, such as the curse of dimensionality and bootstrapping problems. Despite an increasing attempt over the last decade to incorporate behavior (knowledge) transfer in machine learning so that relevant behavior acquired in previous learning experiences can be used to boost task performance in complex tasks, using evolutionary algorithms to facilitate behavior transfer (especially multi-agent behavior transfer) has received little attention. It remains unclear how b...
An objective of transfer learning is to improve and speedup learning on target tasks after traini... more An objective of transfer learning is to improve and speedup learning on target tasks after training on a different, but related source tasks. This research is a study of comparative Neuro-Evolution (NE) methods for transferring evolved multi-agent policies (behaviors) between multi-agent tasks of varying complexity. The efficacy of five variants of two NE methods are compared for multi-agent policy transfer. The NE method variants include using the original versions (search directed by a fitness function), behavioural and genotypic diversity based search to replace objective based search (fitness functions) as well as hybrid objective and diversity (behavioral and genotypic) maintenance based search approaches. The goal of testing these variants to direct policy search is to ascertain an appropriate method for boosting the task performance of transferred multi-agent behaviours. Results indicate that an indirect encoding NE method using hybridized objective based search and behaviora...
The design of effective, robust and autonomous controllers for multi-agent and multi-robot system... more The design of effective, robust and autonomous controllers for multi-agent and multi-robot systems is a long-standing problem in the fields of computational intelligence and robotics. Whilst nature-inspired problem-solving techniques such as reinforcement learning (RL) and evolutionary algorithms (EA) are often used to adapt controllers for solving such tasks, the complexity of such tasks increases with the addition of more agents (or robots) in difficult environments. This is due to specific issues related to task complexity, such as the curse of dimensionality and bootstrapping problems. Despite an increasing attempt over the last decade to incorporate behavior (knowledge) transfer in machine learning so that relevant behavior acquired in previous learning experiences can be used to boost task performance in complex tasks, using evolutionary algorithms to facilitate behavior transfer (especially multi-agent behavior transfer) has received little attention. It remains unclear how b...
An objective of transfer learning is to improve and speedup learning on target tasks after traini... more An objective of transfer learning is to improve and speedup learning on target tasks after training on a different, but related source tasks. This research is a study of comparative Neuro-Evolution (NE) methods for transferring evolved multi-agent policies (behaviors) between multi-agent tasks of varying complexity. The efficacy of five variants of two NE methods are compared for multi-agent policy transfer. The NE method variants include using the original versions (search directed by a fitness function), behavioural and genotypic diversity based search to replace objective based search (fitness functions) as well as hybrid objective and diversity (behavioral and genotypic) maintenance based search approaches. The goal of testing these variants to direct policy search is to ascertain an appropriate method for boosting the task performance of transferred multi-agent behaviours. Results indicate that an indirect encoding NE method using hybridized objective based search and behaviora...
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