Reinforcement Learning for Control of Non-Markovian Cellular Population Dynamics

• URL: http://arxiv.org/abs/2410.08439v3
• Date: Tue, 28 Jan 2025 15:13:10 GMT
• Title: Reinforcement Learning for Control of Non-Markovian Cellular Population Dynamics
• Authors: Josiah C. Kratz, Jacob Adamczyk,
• Abstract summary: We apply reinforcement learning to identify informed dosing strategies to control cell populations evolving under novel non-Markovian dynamics. We find that model-free deep RL is able to recover exact solutions and control cell populations even in the presence of long-range temporal dynamics.
• Score: 1.03590082373586
• License:
• Abstract: Many organisms and cell types, from bacteria to cancer cells, exhibit a remarkable ability to adapt to fluctuating environments. Additionally, cells can leverage a memory of past environments to better survive previously-encountered stressors. From a control perspective, this adaptability poses significant challenges in driving cell populations toward extinction, and thus poses an open question with great clinical significance. In this work, we focus on drug dosing in cell populations exhibiting phenotypic plasticity. For specific dynamical models switching between resistant and susceptible states, exact solutions are known. However, when the underlying system parameters are unknown, and for complex memory-based systems, obtaining the optimal solution is currently intractable. To address this challenge, we apply reinforcement learning (RL) to identify informed dosing strategies to control cell populations evolving under novel non-Markovian dynamics. We find that model-free deep RL is able to recover exact solutions and control cell populations even in the presence of long-range temporal dynamics. To further test our approach in more realistic settings, we demonstrate robust RL-based control strategies in environments with measurement noise and dynamic memory strength.

Related papers

• Online Control in Population Dynamics [32.09385328027713]
  We propose a new framework based on the paradigm of online control. We first characterize a set of linear dynamical systems that can naturally model evolving populations. We then give an efficient gradient-based controller for these systems, with near-optimal regret bounds.
  arXiv  Detail & Related papers  (2024-06-03T21:40:59Z)
• HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments [93.94020724735199]
  HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
  arXiv  Detail & Related papers  (2024-01-23T18:59:43Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics [20.01834405021846]
  Single-cell genomics has advanced our understanding of cellular behavior, catalyzing innovations in treatments and precision medicine. Traditional discrete solvers are hampered by scalability, privacy, and out-of-sample estimation issues. We present a neural network-based solvers, known as neural OT solvers, that parameterize OT maps. We demonstrate its versatility and robustness through applications in cell development studies, cellular drug response modeling, and cross-modality cell translation.
  arXiv  Detail & Related papers  (2023-10-13T17:12:04Z)
• Predictive Experience Replay for Continual Visual Control and Forecasting [62.06183102362871]
  We present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting. We first propose the mixture world model that learns task-specific dynamics priors with a mixture of Gaussians, and then introduce a new training strategy to overcome catastrophic forgetting. Our model remarkably outperforms the naive combinations of existing continual learning and visual RL algorithms on DeepMind Control and Meta-World benchmarks with continual visual control tasks.
  arXiv  Detail & Related papers  (2023-03-12T05:08:03Z)
• The scaling of goals via homeostasis: an evolutionary simulation, experiment and analysis [0.0]
  We propose that evolution pivoted the collective intelligence of cells during morphogenesis into behavioral intelligence by scaling up the goal states at the center of homeostatic processes. We found that these emergent morphogenetic agents exhibit a number of predicted features, including the use of stress propagation dynamics to achieve its target morphology. We propose that this system is a first step toward a quantitative understanding of how evolution scales minimal goal-directed behavior (homeostatic loops) into higher-level problem-solving agents in morphogenetic and other spaces.
  arXiv  Detail & Related papers  (2022-11-15T21:48:44Z)
• Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning [0.0]
  Deep learning is capable of discovering complex control strategies for high-dimensional systems, making it promising for flow control applications. A major challenge associated with RL is that substantial training data must be generated by repeatedly interacting with the target system. We use a data-driven reduced-order model (ROM) in place the true system during RL training to efficiently estimate the optimal policy. We show that the ROM-based control strategy translates well to the true KSE and highlight that the RL agent discovers and stabilizes an underlying forced equilibrium solution of the KSE system.
  arXiv  Detail & Related papers  (2022-05-01T23:25:44Z)
• Information is Power: Intrinsic Control via Information Capture [110.3143711650806]
  We argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model. This objective induces an agent to both gather information about its environment, corresponding to reducing uncertainty, and to gain control over its environment, corresponding to reducing the unpredictability of future world states.
  arXiv  Detail & Related papers  (2021-12-07T18:50:42Z)
• Towards self-organized control: Using neural cellular automata to robustly control a cart-pole agent [62.997667081978825]
  We use neural cellular automata to control a cart-pole agent. We trained the model using deep-Q learning, where the states of the output cells were used as the Q-value estimates to be optimized.
  arXiv  Detail & Related papers  (2021-06-29T10:49:42Z)
• Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks [0.0]
  We show how cellular dynamics help neurons to filter out the intense signals to help neurons keep a stable firing rate. Such an approach might be used in the machine learning domain to improve the robustness of AI systems.
  arXiv  Detail & Related papers  (2021-03-15T05:22:14Z)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.