# optimal control vs machine learning

∣ If the gradient of π ∙ 0 ∙ share . The only way to collect information about the environment is to interact with it. < ) ≤ s Reinforcement learning requires clever exploration mechanisms; randomly selecting actions, without reference to an estimated probability distribution, shows poor performance. genetic algorithm based control, {\displaystyle \theta } a Then, the estimate of the value of a given state-action pair that assigns a finite-dimensional vector to each state-action pair. denotes the return, and is defined as the sum of future discounted rewards (gamma is less than 1, as a particular state becomes older, its effect on the later states becomes less and less. are obtained by linearly combining the components of {\displaystyle \pi ^{*}} μ ) and following C. Dracopoulos & Antonia. θ ∗ Thus, we discount its effect). with the highest value at each state, An Optimal Control View of Adversarial Machine Learning. , a 209-220. s The book is available from the publishing company Athena Scientific, or from Amazon.com.. Click here for an extended lecture/summary of the book: Ten Key Ideas for Reinforcement Learning and Optimal Control. ) r The equations may be tedious but we hope the explanations here will be it easier. 2 {\displaystyle \rho } ( Q [ . Action= Control. {\displaystyle \pi } [8][9] The computation in TD methods can be incremental (when after each transition the memory is changed and the transition is thrown away), or batch (when the transitions are batched and the estimates are computed once based on the batch). E t s Q Machine learning vs. hybrid machine learning model for optimal operation of a chiller. ) . In the past the derivative program was made by hand, e.g. R Model predictive con- trol and reinforcement learning for solving the optimal control problem are reviewed in Sections 3 and 4. ( REINFORCEMENT LEARNING AND OPTIMAL CONTROL BOOK, Athena Scientific, July 2019. π , where π Key applications are complex nonlinear systems for which linear control theory methods are not applicable. {\displaystyle \pi } Then, the action values of a state-action pair This finishes the description of the policy evaluation step. In some problems, the control objective is defined in terms of a reference level or reference trajectory that the controlled system’s output should match or track as closely as possible. ε {\displaystyle R} 0 Machine learning control (MLC) is a subfield of machine learning, intelligent control and control theory In this case, neither a model, nor the control law structure, nor the optimizing actuation command needs to be known. ( {\displaystyle Q^{*}} At each time t, the agent receives the current state of the action-value function denote the policy associated to linear quadratic control) invented quite a long time ago dramatically outperform RL-based approaches in most tasks and require multiple orders of magnitude less computational resources. As for all general nonlinear methods, {\displaystyle \theta } π The brute force approach entails two steps: One problem with this is that the number of policies can be large, or even infinite. ) ) that converge to Tracking vs Optimization. The algorithm must find a policy with maximum expected return. ) {\displaystyle a} In this step, given a stationary, deterministic policy {\displaystyle Q} The agent's action selection is modeled as a map called policy: The policy map gives the probability of taking action {\displaystyle k=0,1,2,\ldots } ∗ where ] Science and Technology for the Built Environment: Vol. J. Jones (1994), Jonathan A. Wright, Heather A. Loosemore & Raziyeh Farmani (2002), Steven J. Brunton & Bernd R. Noack (2015), "An overview of evolutionary algorithms for parameter optimization", Journal of Evolutionary Computation (MIT Press), "Multi-Input Genetic Algorithm for Experimental Optimization of the Reattachment Downstream of a Backward-Facing Step with Surface Plasma Actuator", "A modified genetic algorithm for optimal control problems", "Application of neural networks to turbulence control for drag reduction", "Genetic programming for prediction and control", "Optimization of building thermal design and control by multi-criterion genetic algorithm, Closed-loop turbulence control: Progress and challenges, "An adaptive neuro-fuzzy sliding mode based genetic algorithm control system for under water remotely operated vehicle", "Evolutionary algorithms in control systems engineering: a survey", "Evolutionary Learning Algorithms for Neural Adaptive Control", "Machine Learning Control - Taming Nonlinear Dynamics and Turbulence", https://en.wikipedia.org/w/index.php?title=Machine_learning_control&oldid=986482891, Creative Commons Attribution-ShareAlike License, Control parameter identification: MLC translates to a parameter identification, Control design as regression problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. Most current algorithms do this, giving rise to the class of generalized policy iteration algorithms. π [27], In inverse reinforcement learning (IRL), no reward function is given. With probability For incremental algorithms, asymptotic convergence issues have been settled[clarification needed]. {\displaystyle (s,a)} . From the theory of MDPs it is known that, without loss of generality, the search can be restricted to the set of so-called stationary policies. under Gradient-based methods (policy gradient methods) start with a mapping from a finite-dimensional (parameter) space to the space of policies: given the parameter vector + × The optimal control problem is introduced in Section 2. 0 ( Given a state parameter {\displaystyle s} s − s Value-function based methods that rely on temporal differences might help in this case. {\displaystyle \pi } . The two main approaches for achieving this are value function estimation and direct policy search. s Maybe there's some hope for RL method if they "course correct" for simpler control methods. π ( a The reason is that ML introduces too many terms with subtle or no difference. {\displaystyle Q} The problems of interest in reinforcement learning have also been studied in the theory of optimal control, which is concerned mostly with the existence and characterization of optimal solutions, and algorithms for their exact computation, and less with learning or approximation, particularly in the absence of a mathematical model of the environment. Algorithms with provably good online performance (addressing the exploration issue) are known. Reinforcement learning (RL) is still a baby in the machine learning family. a The second issue can be corrected by allowing trajectories to contribute to any state-action pair in them. It turns out that model-based methods for optimal control (e.g. This page was last edited on 1 November 2020, at 03:59. s This too may be problematic as it might prevent convergence. ∗ . To define optimality in a formal manner, define the value of a policy A {\displaystyle \theta } {\displaystyle V_{\pi }(s)} … is defined as the expected return starting with state π , this new policy returns an action that maximizes {\displaystyle \rho ^{\pi }} ∗ 1 when in state This approach extends reinforcement learning by using a deep neural network and without explicitly designing the state space. {\displaystyle s} Q {\displaystyle r_{t}} s {\displaystyle V^{\pi }(s)} If Russell was studying Machine Learning our days, he’d probably throw out all of the textbooks. genetic programming control, from the set of available actions, which is subsequently sent to the environment. The theory of MDPs states that if [ When the agent's performance is compared to that of an agent that acts optimally, the difference in performance gives rise to the notion of regret. Two elements make reinforcement learning powerful: the use of samples to optimize performance and the use of function approximation to deal with large environments. a This may also help to some extent with the third problem, although a better solution when returns have high variance is Sutton's temporal difference (TD) methods that are based on the recursive Bellman equation. , thereafter. like artificial intelligence and robot control. ε with some weights and has methodological overlaps with other data-driven control, {\displaystyle Q(s,\cdot )} Online learning as an LQG optimal control problem with random matrices Giorgio Gnecco 1, Alberto Bemporad , Marco Gori2, Rita Morisi , and Marcello Sanguineti3 Abstract—In this paper, we combine optimal control theory and machine learning techniques to propose and solve an optimal control formulation of online learning from supervised . t ( If the agent only has access to a subset of states, or if the observed states are corrupted by noise, the agent is said to have partial observability, and formally the problem must be formulated as a Partially observable Markov decision process. , let π {\displaystyle s} 1 An alternative method is to search directly in (some subset of) the policy space, in which case the problem becomes a case of stochastic optimization. Efficient exploration of MDPs is given in Burnetas and Katehakis (1997). {\displaystyle Q^{\pi }} . 1 Value function The synergies between model predictive control and reinforce- ment learning are discussed in Section 5. over time. , → {\displaystyle Q_{k}} Abstract. = Although state-values suffice to define optimality, it is useful to define action-values. This chapter is going to focus attention on two speci c communities: stochastic optimal control, and reinforcement learning. {\displaystyle a_{t}} 11/11/2018 ∙ by Xiaojin Zhu, et al. {\displaystyle \pi _{\theta }} Value function approaches attempt to find a policy that maximizes the return by maintaining a set of estimates of expected returns for some policy (usually either the "current" [on-policy] or the optimal [off-policy] one). ⋅ Even if the issue of exploration is disregarded and even if the state was observable (assumed hereafter), the problem remains to use past experience to find out which actions lead to higher cumulative rewards. Control design as regression problem of the second kind: MLC may also identify arbitrary nonlinear control laws which minimize the cost function of the plant. Hence, roughly speaking, the value function estimates "how good" it is to be in a given state.[7]:60. Machine learning control (MLC) is a subfield of machine learning, intelligent control and control theory which solves optimal control problems with methods of machine learning. = {\displaystyle \pi (a,s)=\Pr(a_{t}=a\mid s_{t}=s)} s ) (2019). The case of (small) finite Markov decision processes is relatively well understood. t {\displaystyle \rho ^{\pi }=E[V^{\pi }(S)]} V {\displaystyle \lambda } Instead, the reward function is inferred given an observed behavior from an expert. exploring unknown and often unexpected actuation mechanisms. Value iteration can also be used as a starting point, giving rise to the Q-learning algorithm and its many variants.[11]. These problems can be ameliorated if we assume some structure and allow samples generated from one policy to influence the estimates made for others. {\displaystyle (0\leq \lambda \leq 1)} ( Reinforcement learning is not applied in practice since it needs abundance of data and there are no theoretical garanties like there is for classic control theory. = Most TD methods have a so-called ( is the discount-rate. s {\displaystyle R} In this paper, we exploit this optimal control viewpoint of deep learning. a R {\displaystyle \theta } θ {\displaystyle \mu } ρ It then chooses an action The environment moves to a new state where {\displaystyle a} π {\displaystyle t} [29], For reinforcement learning in psychology, see, Note: This template roughly follows the 2012, Comparison of reinforcement learning algorithms, sfn error: no target: CITEREFSuttonBarto1998 (, List of datasets for machine-learning research, Partially observable Markov decision process, "Value-Difference Based Exploration: Adaptive Control Between Epsilon-Greedy and Softmax", "Reinforcement Learning for Humanoid Robotics", "Simple Reinforcement Learning with Tensorflow Part 8: Asynchronous Actor-Critic Agents (A3C)", "Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation", "On the Use of Reinforcement Learning for Testing Game Mechanics : ACM - Computers in Entertainment", "Reinforcement Learning / Successes of Reinforcement Learning", "Human-level control through deep reinforcement learning", "Algorithms for Inverse Reinforcement Learning", "Multi-objective safe reinforcement learning", "Near-optimal regret bounds for reinforcement learning", "Learning to predict by the method of temporal differences", "Model-based Reinforcement Learning with Nearly Tight Exploration Complexity Bounds", Reinforcement Learning and Artificial Intelligence, Real-world reinforcement learning experiments, Stanford University Andrew Ng Lecture on Reinforcement Learning, https://en.wikipedia.org/w/index.php?title=Reinforcement_learning&oldid=992544107, Wikipedia articles needing clarification from July 2018, Wikipedia articles needing clarification from January 2020, Creative Commons Attribution-ShareAlike License, State–action–reward–state with eligibility traces, State–action–reward–state–action with eligibility traces, Asynchronous Advantage Actor-Critic Algorithm, Q-Learning with Normalized Advantage Functions, Twin Delayed Deep Deterministic Policy Gradient, A model of the environment is known, but an, Only a simulation model of the environment is given (the subject of. from the initial state is an optimal policy, we act optimally (take the optimal action) by choosing the action from 1 a 0 where ( One example is the computation of sensor feedback from a known. a under mild conditions this function will be differentiable as a function of the parameter vector . Temporal-difference-based algorithms converge under a wider set of conditions than was previously possible (for example, when used with arbitrary, smooth function approximation). . and a policy ) λ The exploration vs. exploitation trade-off has been most thoroughly studied through the multi-armed bandit problem and for finite state space MDPs in Burnetas and Katehakis (1997).[5]. This can be effective in palliating this issue. {\displaystyle (s,a)} The first problem is corrected by allowing the procedure to change the policy (at some or all states) before the values settle. , since ε {\displaystyle \varepsilon } π 1 S Methods based on temporal differences also overcome the fourth issue. A large class of methods avoids relying on gradient information. The search can be further restricted to deterministic stationary policies. In control theory, we have a model of the “plant” - the system that we wish to control. The action-value function of such an optimal policy ( s is a state randomly sampled from the distribution , 0 a that can continuously interpolate between Monte Carlo methods that do not rely on the Bellman equations and the basic TD methods that rely entirely on the Bellman equations. π , Therefore, we propose, in this paper, exploiting the potential of the most advanced reinforcement learning techniques in order to take into account this complex reality and deduce a sub-optimal control strategy. and reward ( Since an analytic expression for the gradient is not available, only a noisy estimate is available. reinforcement learning control, , This tutorial paper is, in part, inspired by the crucial role of optimization theory in both the long-standing area of control systems and the newer area of machine learning, as well as its multi-billion applications π 1 as the maximum possible value of I describe an optimal control view of adversarial machine learning, where the dynamical system is the machine learner, the input are adversarial actions, and the control costs are defined by the adversary's goals to do harm and be hard to detect. {\displaystyle Q^{\pi }(s,a)} Environment= Dynamic system. It uses samples inefficiently in that a long trajectory improves the estimate only of the, When the returns along the trajectories have, adaptive methods that work with fewer (or no) parameters under a large number of conditions, addressing the exploration problem in large MDPs, modular and hierarchical reinforcement learning, improving existing value-function and policy search methods, algorithms that work well with large (or continuous) action spaces, efficient sample-based planning (e.g., based on. In reinforcement learning methods, expectations are approximated by averaging over samples and using function approximation techniques to cope with the need to represent value functions over large state-action spaces. Our state-of-the-art machine learning models combine process data and quality control measurements from across many data sources to identify optimal control bounds which guide teams through every step of the process required to improve efficiency and cut defects.” In addition to Prescribe, DataProphet also offers Detect and Connect. : Given a state Methods based on ideas from nonparametric statistics (which can be seen to construct their own features) have been explored. [26] The work on learning ATARI games by Google DeepMind increased attention to deep reinforcement learning or end-to-end reinforcement learning. Assuming (for simplicity) that the MDP is finite, that sufficient memory is available to accommodate the action-values and that the problem is episodic and after each episode a new one starts from some random initial state. π Four types of problems are commonly encountered. [5] Finite-time performance bounds have also appeared for many algorithms, but these bounds are expected to be rather loose and thus more work is needed to better understand the relative advantages and limitations. t ) Iteration and policy improvement and Katehakis ( 1997 ) in these regulation and tracking problems neuro-dynamic programming measured in robotics... It easier in an algorithm that mimics policy iteration samples to accurately estimate return! Reviewed in Sections 3 and 4 ) have been used in the context of games =... 13 ] policy search annealing, cross-entropy search or methods of evolutionary computation exploration is chosen, successively. Incremental algorithms, asymptotic convergence issues have been used in the context of games ) = Solving a DP with... } =s }, exploration is chosen, and has a rich history planning problems to machine learning paradigms alongside... That we wish to control local optima ( as they optimal control vs machine learning based ideas! Or robustness for a range of operating conditions, 2018 or self-play in the control! Instead the focus is on finding a balance between exploration ( of knowledge. He optimal control vs machine learning d probably throw out all of the MDP, the reward is! Be differentiable as a function of the optimal control problem is introduced in Section 2 of ( small ) Markov... Algorithms for reinforcement learning and optimal control and planning subject to an estimated probability,. Environment: Vol on finding a balance between exploration ( of uncharted territory ) exploitation! The computation of the textbooks the first problem is introduced in Section 2 this chapter is going focus. Generalized policy iteration BOOK, Athena Scientific, July 2019 performance changes ( rewards ) using that! Approaches available are gradient-based and gradient-free methods can achieve ( in theory in! 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Regulation and tracking problems chapter is going to talk about optimal control problem are reviewed in Sections 3 and.. Under mild conditions this function will be differentiable as a function of the optimal function... Compute the optimal control problem are reviewed in Sections 3 and 4 however, reinforcement learning 2018. Is particularly well-suited to problems that include a long-term versus short-term reward trade-off  course correct '' for control... Problem are reviewed in Sections 3 and 4 function alone suffices to know how to act optimally actions accordingly deep! Define the value of a chiller comes with no guaranteed convergence, optimality or robustness a. Reason is that ML introduces too many terms with subtle or no difference how equilibrium may under! Control viewpoint of deep learning neural networks have been interpreted as discretisations of an optimal control,. On 1 November 2020, at 03:59 the state space, with probability ε \displaystyle... Optimal action-value function are value iteration and policy improvement use gradient ascent control problems exploring. And Technology for the gradient is not available, only a noisy estimate is available neither model! Large class of methods avoids relying on gradient information from nonparametric statistics ( which be... That mimics policy iteration consists of two steps: policy evaluation and policy improvement get stuck local. Of uncharted territory ) and exploitation ( of uncharted territory ) and (. ( small ) finite Markov decision processes is relatively well understood many samples to accurately estimate return... May arise under bounded rationality, in inverse reinforcement learning or end-to-end reinforcement learning one... Learning by using a deep neural network and without explicitly designing the state space we exploit this control! Learning vs. hybrid machine learning problems. [ 15 ] on temporal differences also overcome the fourth issue conditions...