基于强化学习的精密水族箱鱼类生长轨迹跟踪
Fish Growth Trajectory Tracking via Reinforcement Learning in Precision Aquaculture
摘要：
本文研究了在一个典型的生物能量生长模型下，通过强化学习跟踪鱼类生长轨迹的方法。由于养殖条件复杂，环境因素不确定，如温度、溶解氧、非离子氨等，以及鱼类生长模型的多输入、强非线性耦合等，基于模型的控制方法无法有效解决精密养殖中的生长轨迹跟踪问题。为此，我们制定的增长轨迹跟踪问题的采样数据最优控制使用离散状态-动作对马尔可夫决策过程。我们提出了两个Q-学习算法，学习最优控制策略从采样数据的鱼的生长轨迹在鱼的生命周期的每个阶段，从青少年到所需的市场重量在水产养殖环境。Q-学习方案学习最优的饲养控制策略，以在笼子里养殖的鱼的生长速率和最优的饲养速率控制策略与最优的温度曲线的水产养殖鱼的生长速率在坦克。仿真结果表明，两种Q学习策略都能在较小的进给量下实现较高的轨迹跟踪性能。
英文摘要：
This paper studies the fish growth trajectory tracking via reinforcement learning under a representative bioenergetic growth model. Due to the complex aquaculture condition and uncertain environmental factors such as temperature, dissolved oxygen, un-ionized ammonia, and strong nonlinear couplings, including multi-inputs of the fish growth model, the growth trajectory tracking problem can not be efficiently solved by the model-based control approaches in precision aquaculture. To this purpose, we formulate the growth trajectory tracking problem as sampled-data optimal control using discrete state-action pairs Markov decision process. We propose two Q-learning algorithms that learn the optimal control policy from the sampled data of the fish growth trajectories at every stage of the fish life cycle from juveniles to the desired market weight in the aquaculture environment. The Q-learning scheme learns the optimal feeding control policy to fish growth rate cultured in cages and the optimal feeding rate control policy with an optimal temperature profile for the aquaculture fish growth rate in tanks. The simulation results demonstrate that both Q-learning strategies achieve high trajectory tracking performance with less amount feeding rates.
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