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Adaptive reservoir computing through evolution and learning
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DOI：10.1016/j.neucom.2012.09.022.png)
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The development of real-world, fully autonomous agents would require mechanisms that would offer generalization capabilities from experience, suitable for a large range of machine learning tasks, like those from the areas of supervised and reinforcement learning. Such capacities could be offered by parametric function approximators that could either model the environment or the agent's policy. To promote autonomy, these structures should be adapted to the problem at hand with no or little human expert input. Towards this goal, we propose an adaptive function approximator method for developing appropriate neural networks in the form of reservoir computing systems through evolution and learning. Our neuro-evolution of augmenting reservoirs approach comprises of several ideas, successful on their own, in an effort to develop an algorithm that could handle a large range of problems, more efficiently. In particular, we use the neuro-evolution of augmented topologies algorithm as a meta-search method for the adaptation of echo state networks for handling problems to be encountered by autonomous entities. We test our approach on several test-beds from the realms of time series prediction and reinforcement learning. We compare our methodology against similar state-of-the-art algorithms with promising results. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Reservoir computing
Echo state networks
Neuroevolution
Evolutionary computation
Reinforcement learning
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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