论文标题
大规模概率布尔网络稳定的深度加强学习
Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks
论文作者
论文摘要
将概率布尔网络（PBN）引导到所需状态的能力对于诸如癌症生物学中的靶向治疗剂的应用很重要。已提出了加强学习（RL）作为一个框架，该框架解决了一个离散的时间最佳控制问题作为马尔可夫决策过程。我们专注于一个由无模型深的RL方法驱动的集成框架，该方法可以解决控制问题的不同口味（例如，有或没有控制输入，吸引子状态或状态空间的子集作为目标域）。该方法对下一个状态的概率分布不可知，因此它不使用概率过渡矩阵。在训练期间，时间步骤或环境（PBN）之间的时间步骤或环境之间的相互作用是线性的。确实，我们探讨了深度RL方法（集合）稳定大型PBN的可扩展性，并在大型网络上证明了成功的控制，包括具有200个节点的转移性黑色素瘤PBN。
The ability to direct a Probabilistic Boolean Network (PBN) to a desired state is important to applications such as targeted therapeutics in cancer biology. Reinforcement Learning (RL) has been proposed as a framework that solves a discrete-time optimal control problem cast as a Markov Decision Process. We focus on an integrative framework powered by a model-free deep RL method that can address different flavours of the control problem (e.g., with or without control inputs; attractor state or a subset of the state space as the target domain). The method is agnostic to the distribution of probabilities for the next state, hence it does not use the probability transition matrix. The time complexity is linear on the time steps, or interactions between the agent (deep RL) and the environment (PBN), during training. Indeed, we explore the scalability of the deep RL approach to (set) stabilization of large-scale PBNs and demonstrate successful control on large networks, including a metastatic melanoma PBN with 200 nodes.
