scholarly journals Deep reinforcement learning for the control of microbial co-cultures in bioreactors

2018 ◽  
Author(s):  
Neythen J. Treloar ◽  
Alexander J.H. Fedorec ◽  
Brian P. Ingalls ◽  
Chris P. Barnes
Keyword(s):  
Reinforcement Learning ◽  
Microbial Communities ◽  
Control Policy ◽  
Continuous Control ◽  
Natural Ecosystems ◽  
Model Free ◽  
Pure Cultures ◽  
Integral Controller ◽  
Proportional Integral Controller ◽  
Bang Bang Control

AbstractMulti-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity (in comparison with pure cultures) and allow for the reduction of metabolic load by compartmentalising bioprocesses between multiple sub-populations. Despite these benefits, co-cultures are rarely used in practice because control over the constituent species of an assembled community has proven challenging. Here we demonstrate, in silico, the efficacy of an approach from artificial intelligence – reinforcement learning – in the control of co-cultures within continuous bioreactors. We confirm that feedback via reinforcement learning can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform traditional proportional integral controller with continuous control, when faced with infrequent sampling. Further, we demonstrate that a satisfactory control policy can be learned in one twenty-four hour experiment, by running five bioreactors in parallel. Finally, we show that reinforcement learning can directly optimise the output of a co-culture bioprocess. Overall, reinforcement learning is a promising technique for the control of microbial communities.

scholarly journals Stochastic Actor-Executor-Critic for Image-to-Image Translation

2021 ◽  
Author(s):  
Ziwei Luo ◽  
Jing Hu ◽  
Xin Wang ◽  
Siwei Lyu ◽  
Bin Kong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Control Policy ◽  
High Dimensional ◽  
Continuous Control ◽  
Continuous Space ◽  
Model Free ◽  
Recent Success ◽  
Image Translation ◽  
Continuous State ◽  
And Control

Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces. In this paper, we draw inspiration from the recent success of the maximum entropy reinforcement learning framework designed for challenging continuous control problems to develop stochastic policies over high dimensional continuous spaces including image representation, generation, and control simultaneously. Central to this method is the Stochastic Actor-Executor-Critic (SAEC) which is an off-policy actor-critic model with an additional executor to generate realistic images. Specifically, the actor focuses on the high-level representation and control policy by a stochastic latent action, as well as explicitly directs the executor to generate low-level actions to manipulate the state. Experiments on several image-to-image translation tasks have demonstrated the effectiveness and robustness of the proposed SAEC when facing high-dimensional continuous space problems.

scholarly journals Deterministic Value-Policy Gradients

2020 ◽  
Vol 34 (04) ◽  
pp. 3316-3323
Author(s):  
Qingpeng Cai ◽  
Ling Pan ◽  
Pingzhong Tang
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Infinite Horizon ◽  
Gradient Algorithm ◽  
Continuous Control ◽  
Model Bias ◽  
Model Free ◽  
Policy Gradient ◽  
Analytical Gradients

Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG algorithm suffers from high sample complexity. In this paper we consider the deterministic value gradients to improve the sample efficiency of deep reinforcement learning algorithms. Previous works consider deterministic value gradients with the finite horizon, but it is too myopic compared with infinite horizon. We firstly give a theoretical guarantee of the existence of the value gradients in this infinite setting. Based on this theoretical guarantee, we propose a class of the deterministic value gradient algorithm (DVG) with infinite horizon, and different rollout steps of the analytical gradients by the learned model trade off between the variance of the value gradients and the model bias. Furthermore, to better combine the model-based deterministic value gradient estimators with the model-free deterministic policy gradient estimator, we propose the deterministic value-policy gradient (DVPG) algorithm. We finally conduct extensive experiments comparing DVPG with state-of-the-art methods on several standard continuous control benchmarks. Results demonstrate that DVPG substantially outperforms other baselines.

scholarly journals End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

2019 ◽  
Vol 33 ◽  
pp. 3387-3395 ◽  
Author(s):  
Richard Cheng ◽  
Gábor Orosz ◽  
Richard M. Murray ◽  
Joel W. Burdick
Keyword(s):  
Reinforcement Learning ◽  
System Dynamics ◽  
Learning Process ◽  
High Performance ◽  
Continuous Control ◽  
Barrier Functions ◽  
Synthesis Algorithm ◽  
Vehicle Communication ◽  
Model Free ◽  
Vehicle To Vehicle

Reinforcement Learning (RL) algorithms have found limited success beyond simulated applications, and one main reason is the absence of safety guarantees during the learning process. Real world systems would realistically fail or break before an optimal controller can be learned. To address this issue, we propose a controller architecture that combines (1) a model-free RL-based controller with (2) model-based controllers utilizing control barrier functions (CBFs) and (3) online learning of the unknown system dynamics, in order to ensure safety during learning. Our general framework leverages the success of RL algorithms to learn high-performance controllers, while the CBF-based controllers both guarantee safety and guide the learning process by constraining the set of explorable polices. We utilize Gaussian Processes (GPs) to model the system dynamics and its uncertainties. Our novel controller synthesis algorithm, RL-CBF, guarantees safety with high probability during the learning process, regardless of the RL algorithm used, and demonstrates greater policy exploration efficiency. We test our algorithm on (1) control of an inverted pendulum and (2) autonomous carfollowing with wireless vehicle-to-vehicle communication, and show that our algorithm attains much greater sample efficiency in learning than other state-of-the-art algorithms and maintains safety during the entire learning process.

scholarly journals End-to-End Deep Reinforcement Learning for Image-Based UAV Autonomous Control

2021 ◽  
Vol 11 (18) ◽  
pp. 8419
Author(s):  
Jiang Zhao ◽  
Jiaming Sun ◽  
Zhihao Cai ◽  
Longhong Wang ◽  
Yingxun Wang
Keyword(s):  
Reinforcement Learning ◽  
Network Architecture ◽  
Control Method ◽  
Control Policy ◽  
Input Image ◽  
Autonomous Control ◽  
Policy Network ◽  
Model Free ◽  
Control Command ◽  
End To End

To achieve the perception-based autonomous control of UAVs, schemes with onboard sensing and computing are popular in state-of-the-art work, which often consist of several separated modules with respective complicated algorithms. Most methods depend on handcrafted designs and prior models with little capacity for adaptation and generalization. Inspired by the research on deep reinforcement learning, this paper proposes a new end-to-end autonomous control method to simplify the separate modules in the traditional control pipeline into a single neural network. An image-based reinforcement learning framework is established, depending on the design of the network architecture and the reward function. Training is performed with model-free algorithms developed according to the specific mission, and the control policy network can map the input image directly to the continuous actuator control command. A simulation environment for the scenario of UAV landing was built. In addition, the results under different typical cases, including both the small and large initial lateral or heading angle offsets, show that the proposed end-to-end method is feasible for perception-based autonomous control.

scholarly journals Control of chaotic systems by deep reinforcement learning

2019 ◽  
Vol 475 (2231) ◽  
pp. 20190351 ◽  
Author(s):  
M. A. Bucci ◽  
O. Semeraro ◽  
A. Allauzen ◽  
G. Wisniewski ◽  
L. Cordier ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Bluff Body ◽  
Initial Conditions ◽  
Control Policy ◽  
Chaotic Regime ◽  
Model Free ◽  
Local Measurements ◽  
Target States ◽  
The One ◽  
Learning Principles

Deep reinforcement learning (DRL) is applied to control a nonlinear, chaotic system governed by the one-dimensional Kuramoto–Sivashinsky (KS) equation. DRL uses reinforcement learning principles for the determination of optimal control solutions and deep neural networks for approximating the value function and the control policy. Recent applications have shown that DRL may achieve superhuman performance in complex cognitive tasks. In this work, we show that using restricted localized actuation, partial knowledge of the state based on limited sensor measurements and model-free DRL controllers, it is possible to stabilize the dynamics of the KS system around its unstable fixed solutions, here considered as target states. The robustness of the controllers is tested by considering several trajectories in the phase space emanating from different initial conditions; we show that DRL is always capable of driving and stabilizing the dynamics around target states. The possibility of controlling the KS system in the chaotic regime by using a DRL strategy solely relying on local measurements suggests the extension of the application of RL methods to the control of more complex systems such as drag reduction in bluff-body wakes or the enhancement/diminution of turbulent mixing.

scholarly journals General Purpose Low-Level Reinforcement Learning Control for Multi-Axis Rotor Aerial Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4560
Author(s):  
Chen-Huan Pi ◽  
Yi-Wei Dai ◽  
Kai-Chun Hu ◽  
Stone Cheng
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Motion Capture ◽  
Flight Control ◽  
Control Policy ◽  
General Purpose ◽  
Position Information ◽  
Low Level ◽  
Model Free ◽  
Aerial Vehicles

This paper proposes a multipurpose reinforcement learning based low-level multirotor unmanned aerial vehicles control structure constructed using neural networks with model-free training. Other low-level reinforcement learning controllers developed in studies have only been applicable to a model-specific and physical-parameter-specific multirotor, and time-consuming training is required when switching to a different vehicle. We use a 6-degree-of-freedom dynamic model combining acceleration-based control from the policy neural network to overcome these problems. The UAV automatically learns the maneuver by an end-to-end neural network from fusion states to acceleration command. The state estimation is performed using the data from on-board sensors and motion capture. The motion capture system provides spatial position information and a multisensory fusion framework fuses the measurement from the onboard inertia measurement units for compensating the time delay and low update frequency of the capture system. Without requiring expert demonstration, the trained control policy implemented using an improved algorithm can be applied to various multirotors with the output directly mapped to actuators. The algorithm’s ability to control multirotors in the hovering and the tracking task is evaluated. Through simulation and actual experiments, we demonstrate the flight control with a quadrotor and hexrotor by using the trained policy. With the same policy, we verify that we can stabilize the quadrotor and hexrotor in the air under random initial states.

scholarly journals Training Champion-level Race Car Drivers Using Deep Reinforcement Learning

2021 ◽  
Author(s):  
Peter Wurman ◽  
Samuel Barrett ◽  
Kenta Kawamoto ◽  
James MacGlashan ◽  
Kaushik Subramanian ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Learning Algorithm ◽  
Integrated Control ◽  
Extreme Case ◽  
Control Policy ◽  
Reward Function ◽  
Race Car ◽  
Model Free ◽  
Race Car Drivers

Abstract Many potential applications of artificial intelligence involve making real-time decisions in physical systems. Automobile racing represents an extreme case of real-time decision making in close proximity to other highly-skilled drivers while near the limits of vehicular control. Racing simulations, such as the PlayStation game Gran Turismo, faithfully reproduce the nonlinear control challenges of real race cars while also encapsulating the complex multi-agent interactions. We attack, and solve for the first time, the simulated racing challenge using model-free deep reinforcement learning. We introduce a novel reinforcement learning algorithm and enhance the learning process with mixed scenario training to encourage the agent to incorporate racing tactics into an integrated control policy. In addition, we construct a reward function that enables the agent to adhere to the sport's under-specified racing etiquette rules. We demonstrate the capabilities of our agent, GT Sophy, by winning two of three races against four of the world's best Gran Turismo drivers and being competitive in the overall team score. By showing that these techniques can be successfully used to train championship-level race car drivers, we open up the possibility of their use in other complex dynamical systems and real-world applications.

scholarly journals Open Loop Position Control of Soft Continuum Arm Using Deep Reinforcement Learning

2018 ◽  
Author(s):  
Sreeshankar Satheeshbabu ◽  
Naveen Kumar Uppalapati ◽  
Girish Chowdhary ◽  
Girish Krishnan
Keyword(s):  
Reinforcement Learning ◽  
Position Control ◽  
Numerical Models ◽  
Control Policy ◽  
Open Loop ◽  
External Loading ◽  
Q Learning ◽  
Model Free ◽  
Experience Replay ◽  
The Continuum

Soft robots undergo large nonlinear spatial deformations due to both inherent actuation and external loading. The physics underlying these deformations is complex, and often requires intricate analytical and numerical models. The complexity of these models may render traditional model based control difficult and unsuitable. Model-free methods offer an alternative for analyzing the behavior of such complex systems without the need for elaborate modeling techniques.In this paper, we present a model-free approach for open loop position control of a soft spatial continuum arm, based on deep reinforcement learning. The continuum arm is pneumatically actuated and attains a spatial workspace by a combination ofunidirectional bending and bidirectional torsional deformation. We use Deep-Q Learning with experience replay to train the system in simulation. The efficacy and robustness of the control policy obtained from the system is validated both in simulation and on the continuum arm prototype for varying external loading conditions

scholarly journals Solving Continuous Control with Episodic Memory

2021 ◽  
Author(s):  
Igor Kuznetsov ◽  
Andrey Filchenkov
Keyword(s):  
Reinforcement Learning ◽  
Episodic Memory ◽  
Data Structures ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Continuous Control ◽  
Improve Performance ◽  
The Past ◽  
Model Free ◽  
Discrete Action

Episodic memory lets reinforcement learning algorithms remember and exploit promising experience from the past to improve agent performance. Previous works on memory mechanisms show benefits of using episodic-based data structures for discrete action problems in terms of sample-efficiency. The application of episodic memory for continuous control with a large action space is not trivial. Our study aims to answer the question: can episodic memory be used to improve agent's performance in continuous control? Our proposed algorithm combines episodic memory with Actor-Critic architecture by modifying critic's objective. We further improve performance by introducing episodic-based replay buffer prioritization. We evaluate our algorithm on OpenAI gym domains and show greater sample-efficiency compared with the state-of-the art model-free off-policy algorithms.

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