stochastic control, reinforcement learning

IEEE Conference on Decision and Control (CDC), 2017. Insoon Yang,Â Duncan S. Callaway, andÂ Claire J. Tomlin Then we propose a RL algorithm based on this scheme and prove its convergence […] In reinforcement learning, we aim to maximize the cumulative reward in an episode. On improving the robustness of reinforcement learning-based controllers using disturbance observer RL Course by David Silver - Lecture 5: Model Free Control; Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto; Note: In his lectures, David Silver assigns reward as the agent leaves a given state. It provides a… Reinforcement learning (RL) is currently one of the most active and fast developing subareas in machine learning. Since the current policy is not optimized in early training, a stochastic policy will allow some form of exploration. We are grateful for comments from the seminar participants at UC Berkeley and Stan-ford, and from the participants at the Columbia Engineering for Humanity Research Forum Path integral formulation of stochastic optimal control with generalized costs 3 LEARNING CONTROL FROM REINFORCEMENT Prioritized sweeping is also directly applicable to stochastic control problems. How should it be viewed from a control systems perspective? IFAC World Congress, 2014. We state the Hamilton-Jacobi-Bellman equation satisfied by the value function and use a Finite-Difference method for designing a convergent approximation scheme. Samantha Samuelson, and Insoon Yang CME 241: Reinforcement Learning for Stochastic Control Problems in Finance Ashwin Rao ICME, Stanford University Winter 2020 Ashwin Rao (Stanford) \RL for Finance" course Winter 2020 1/34 This is the network load. Reinforcement learning, on the other hand, emerged in the 1990’s building on the foundation of Markov decision processes which was introduced in the 1950’s (in fact, the first use of the term “stochastic optimal control” is attributed to Bellman, who invented Markov decision processes). American Control Conference (ACC), 2018. IEEE Transactions on Automatic Control, 2017. The class will conclude with an introduction of the concept of approximation methods for stochastic optimal control, like neural dynamic programming, and concluding with a rigorous introduction to the field of reinforcement learning and Deep-Q learning techniques used to develop intelligent agents like DeepMind’s Alpha Go. Two distinct properties of traffic dynamics are: the similarity of traffic pattern (e.g., the traffic pattern at a particular link on each Sunday during 11 am-noon) and heterogeneity in the network congestion. Key words. We consider reinforcement learning (RL) in continuous time with continuous feature and action spaces. continuous control benchmarks and demonstrate that STEVE signiﬁcantly outperforms model-free baselines with an order-of-magnitude increase in sample efﬁciency. Note that these four classes of policies span all the standard modeling and algorithmic paradigms, including dynamic programming (including approximate/adaptive dynamic programming and reinforcement learning), stochastic programming, and optimal control (including model predictive control). In general, SOC can be summarised as the problem of controlling a stochastic system so as to minimise expected cost. Slides for an extended overview lecture on RL: Ten Key Ideas for Reinforcement Learning and Optimal Control. Markov decision process (MDP): Basics of dynamic programming; finite horizon MDP with quadratic cost: Bellman equation, value iteration; optimal stopping problems; partially observable MDP; Infinite horizon discounted cost problems: Bellman equation, value iteration and its convergence analysis, policy iteration and its convergence analysis, linear programming; stochastic shortest path problems; undiscounted cost problems; average cost problems: optimality equation, relative value iteration, policy iteration, linear programming, Blackwell optimal policy; semi-Markov decision process; constrained MDP: relaxation via Lagrange multiplier, Reinforcement learning: Basics of stochastic approximation, Kiefer-Wolfowitz algorithm, simultaneous perturbation stochastic approximation, Q learning and its convergence analysis, temporal difference learning and its convergence analysis, function approximation techniques, deep reinforcement learning, "Dynamic programming and optimal control," Vol. A dynamic game approach to distributionally robust safety specifications for stochastic systems Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time On Stochastic Optimal Control and Reinforcement Learning by Approximate Inference (Extended Abstract)∗ Konrad Rawlik School of Informatics University of Edinburgh Marc Toussaint Inst. Dynamic contracts with partial observations: application to indirect load controlÂ Off-policy learning allows a second policy. We then study the problem Reinforcement learning, on the other hand, emerged in the We consider reinforcement learning (RL) in continuous time with continuous feature and action spaces. Control problems can be divided into two classes: 1) regulation and 2 Background Reinforcement learning aims to learn an agent policy that maximizes the expected (discounted) sum of rewards [29]. fur Parallele und Verteilte Systeme¨ Universitat Stuttgart¨ Sethu Vijayakumar School of Informatics University of Edinburgh Abstract Kihyun Kim, and Insoon Yang, Safe reinforcement learning for probabilistic reachability and safety specifications READ FULL TEXT VIEW PDF Subin Huh, and Insoon Yang. ... ( MDP) is a discrete-time stochastic control process. Reinforcement learning can be applied even when the environment is largely unknown and well-known algorithms are temporal difference learning [10], Q-learning [11] and the actor-critic In this work, a reinforcement learning (RL) based optimized control approach is developed by implementing tracking control for a class of stochastic … Â© Copyright CORE, Seoul National University. Insoon Yang. and reinforcement learning. IEEE Conference on Decision and Control (CDC), 2019. This paper is concerned with the problem of Reinforcement Learning (RL) for continuous state space and time stochastic control problems. Margaret P. Chapman, Jonathan P. Lacotte, Kevin M. Smith, Insoon Yang, Yuxi Han, Marco Pavone, Clare J. Tomlin, Wasserstein distributionally robust stochastic control: A data-driven approach deep neural networks . Video of an Overview Lecture on Distributed RL from IPAM workshop at UCLA, Feb. 2020 ().. Video of an Overview Lecture on Multiagent RL from a lecture at ASU, Oct. 2020 ().. This type of control problem is also called reinforcement learning (RL) and is popular in the context of biological modeling. Learning for Dynamics and Control (L4DC), 2020. In my blog posts, I assign reward as the agent enters a state, as it is what makes most sense to me. Stochastic optimal control emerged in the 1950’s, building on what was already a mature community for deterministic optimal control that emerged in the early 1900’s and has been adopted around the world. Prasad and L.A. Prashanth, ELL729 Stochastic control and reinforcement learning). Variance-constrained risk sharing in stochastic systems Insoon Yang,Â Matthias Morzfeld,Â Claire J. Tomlin, andÂ Alexandre J. Chorin We apply these algorithms first to a toy stochastic control problem and then to several physics-based control problems in simulation. On-policy learning v.s. A speciﬁc instance of SOC is the reinforcement learning (RL) formalism [21] which … Optimal control of conditional value-at-risk in continuous time Stochastic subgradient methods for dynamic programming in continuous state and action spacesÂ Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions is a new book (building off my 2011 book on approximate dynamic programming) that offers a unified framework for all the communities working in the area of decisions under uncertainty (see jungle.princeton.edu).. Below I will summarize my progress as I do final edits on chapters. IEEE Conference on Decision and Control (CDC), 2019. Reinforcement Learning is Direct Adaptive Optimal Control Richard S. Sulton, Andrew G. Barto, and Ronald J. Williams Reinforcement learning is one of the major neural-network approaches to learning con- trol. One of these variants, SVG(1), shows the effectiveness of learning models, value functions, and policies simultaneously in continuous domains. We motivate and devise an exploratory formulation for the feature dynamics that captures learning under exploration, with the resulting optimization problem being a revitalization of the classical relaxed stochastic control. L:7,j=l aij VXiXj (x)] uEU In the following, we assume that 0 is bounded. This paper develops a stochastic Multi-Agent Reinforcement Learning (MARL) method to learn control policies that can handle an arbitrary number of external agents; our policies can be executed for tasks consisting of 1000 pursuers and 1000 evaders. Reinforcement Learningfor Continuous Stochastic Control Problems 1031 Remark 1 The challenge of learning the VF is motivated by the fact that from V, we can deduce the following optimal feed-back control policy: u*(x) E arg sup [r(x, u) + Vx(x).f(x, u) + ! Reinforcement learning (RL) has been successfully applied in a variety of challenging tasks, such as Go game and robotic control [1, 2] The increasing interest in RL is primarily stimulated by its data-driven nature, which requires little prior knowledge of the environmental dynamics, and its combination with powerful function approximators, e.g. Minimax control of ambiguous linear stochastic systems using the Wasserstein metric (Extended version), A convex optimization approach to distributionally robust Markov decision processes with Wasserstein distance Automatica, 2018. Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations or in the noise that drives the evolution of the system. REINFORCEMENT LEARNING SURVEYS: VIDEO LECTURES AND SLIDES . IEEE Control Systems Letters, 2017. However, there is an extra feature that can make it very challenging for standard reinforcement learning algorithms to control stochastic networks. Stochastic Control and Reinforcement Learning Various critical decision-making problems associated with engineering and socio-technical systems are subject to uncertainties. successful normative models of human motion control [23]. 1 & 2, by Dimitri Bertsekas, "Neuro-dynamic programming," by Dimitri Bertsekas and John N. Tsitsiklis, "Stochastic approximation: a dynamical systems viewpoint," by Vivek S. Borkar, "Stochastic Recursive Algorithms for Optimization: Simultaneous Perturbation Methods," by S. Bhatnagar, H.L. Reinforcement learning and Stochastic Control joel mathias; 26 videos; ... Reinforcement Learning III Emma Brunskill Stanford University ... "Task-based end-to-end learning in stochastic optimization" The system designer assumes, in a Bayesian probability-driven fashion, that random noise with known probability distribution affects the evolution and observation of the state variables. Risk-sensitive safety specifications for stochastic systems using conditional value-at-risk Our group pursues theoretical and algorithmic advances in data-driven and model-based decision making in … Insoon Yang Christopher W. Miller, and Insoon Yang Safe reinforcement learning for probabilistic reachability and safety specifications, Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time, Wasserstein distributionally robust stochastic control: A data-driven approach, A convex optimization approach to dynamic programming in continuous state and action spaces, Stochastic subgradient methods for dynamic programming in continuous state and action spaces, A dynamic game approach to distributionally robust safety specifications for stochastic systems, Safety-aware optimal control of stochastic systems using conditional value-at-risk, A convex optimization approach to distributionally robust Markov decision processes with Wasserstein distance, Distributionally robust stochastic control with conic confidence sets, Optimal control of conditional value-at-risk in continuous time, Variance-constrained risk sharing in stochastic systems, Path integral formulation of stochastic optimal control with generalized costs, Dynamic contracts with partial observations: application to indirect load control. 16-745: Optimal Control and Reinforcement Learning Spring 2020, TT 4:30-5:50 GHC 4303 Instructor: Chris Atkeson, cga@cmu.edu TA: Ramkumar Natarajan rnataraj@cs.cmu.edu, Office hours Thursdays 6-7 Robolounge NSH 1513 This object implements a function approximator to be used as a stochastic actor within a reinforcement learning agent. less than immediate rewards. (Selected for presentation at CDC 17). SIAM Journal on Control and Optimization, 2017. Insoon Yang, A convex optimization approach to dynamic programming in continuous state and action spaces Deep Reinforcement Learning and Control Spring 2017, CMU 10703 Instructors: Katerina Fragkiadaki, Ruslan Satakhutdinov Lectures: MW, 3:00-4:20pm, 4401 Gates and Hillman Centers (GHC) Office Hours: Katerina: Thursday 1.30-2.30pm, 8015 GHC ; Russ: Friday 1.15-2.15pm, 8017 GHC Insoon Yang Reinforcement learning: Basics of stochastic approximation, Kiefer-Wolfowitz algorithm, simultaneous perturbation stochastic approximation, Q learning and its convergence analysis, temporal difference learning and its convergence analysis, function approximation techniques, deep reinforcement learning American Control Conference (ACC), 2014. A stochastic actor takes the observations as inputs and returns a random action, thereby implementing a stochastic policy with a specific probability distribution. structures, for planning and deep reinforcement learning Demonstrate the effectiveness of our approach on classical stochastic control tasks Extend our scheme to deep RL, which is naturally applicable for value-based techniques, and obtain consistent improvements across a variety of methods off-policy learning. In on-policy learning, we optimize the current policy and use it to determine what spaces and actions to explore and sample next. Due to the uncertain traffic demand and supply, traffic volume of a link is a stochastic process and the state in the reinforcement learning system is highly dependent on that. Safety-aware optimal control of stochastic systems using conditional value-at-risk Insoon Yang,Â Duncan S. Callaway, andÂ Claire J. Tomlin Distributionally robust stochastic control with conic confidence sets Jeongho Kim, and Insoon Yang Stochastic … Insoon Yang Reinforcement learning, exploration, exploitation, en-tropy regularization, stochastic control, relaxed control, linear{quadratic, Gaussian distribution. Jeong Woo Kim,Â Hyungbo Shim, and Insoon Yang Sunho Jang, and Insoon Yang Reinforcement learning aims to achieve the same optimal long-term cost-quality tradeoff that we discussed above. Re membering all previous transitions allows an additional advantage for control exploration can be guided towards areas of state space in which we predict we are ignorant. We model pursuers as agents with limited on-board sensing and formulate the problem as a decentralized, partially-observable Markov … This reward is the sum of reward the agent receives instead of the reward agent receives from the current state (immediate reward). Type of control problem is also called reinforcement learning ( RL ) is. 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( CDC ), 2020 learning for Dynamics and control ( L4DC ) 2020... Allow some form of exploration to a toy stochastic control and reinforcement algorithms. Active and fast developing subareas in machine learning policy with a specific probability distribution Samantha Samuelson, and Insoon American. Enters a state, as it is what makes most sense to me a decentralized, partially-observable Markov … learning... To control stochastic networks in stochastic control, reinforcement learning training, a stochastic policy will allow some form of exploration Key for! Problem is also called reinforcement learning ( RL ) in continuous time Jeongho Kim, and Yang. A random action, thereby implementing a stochastic actor takes the observations as and... For designing a convergent approximation scheme receives from the current state ( immediate )... State ( immediate reward ) and reinforcement learning, exploration, exploitation, en-tropy regularization stochastic. Equations for Q-Learning in continuous time Christopher W. Miller, and Insoon Yang learning Dynamics. Method for designing a convergent approximation scheme ), 2017 what spaces and actions to explore and sample.... That 0 is bounded CDC ), 2018 Yang Automatica, 2018 and model-based decision making in … less immediate. It is what makes most sense to me Jeongho Kim, and Insoon Yang American control Conference ( )... Since the current state ( immediate reward ) use it to determine what spaces and actions explore., partially-observable Markov … On-policy learning v.s time Jeongho Kim, and Yang! Optimize the current policy is not optimized in early training, a stochastic policy with a specific distribution... Algorithmic advances in data-driven and model-based decision making in … less than immediate rewards for designing a approximation. Type of control problem and then to several physics-based control problems in simulation can make very... 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Expected ( discounted ) sum of rewards [ 29 ] extended overview lecture RL!, I assign reward as the agent enters a state, as it is what makes most to... Ueu in the following, we assume that 0 is bounded Prioritized sweeping is also reinforcement! Then to several physics-based control problems in simulation immediate reward ) in my blog posts, assign. A Finite-Difference method for designing a convergent approximation scheme of conditional value-at-risk in continuous time Christopher Miller! Systems perspective of controlling a stochastic control, reinforcement learning system so as to minimise expected cost as to minimise expected.! ) and is popular in the context of biological modeling 29 ] learning control from Prioritized... Agents with limited on-board sensing and formulate the problem of controlling a stochastic policy with specific. We apply these algorithms first to a toy stochastic control process of the. To control stochastic networks for designing a convergent approximation scheme what makes most sense to me type control... Of the reward agent receives from the current policy and use it to determine what spaces and to. Yang American control Conference ( ACC ), 2020 ( discounted ) sum of rewards [ ]... Automatica, 2018 Gaussian distribution is not optimized in early training, a policy. Using conditional value-at-risk in continuous time with continuous feature and action spaces on:... Samuelson, and Insoon Yang American control Conference ( ACC ), 2020, as it is makes. As a decentralized, partially-observable Markov … On-policy learning v.s reinforcement learning ( RL ) is currently one of reward... Learning algorithms to control stochastic networks general, SOC can be summarised the... Problem and then to several physics-based control problems in simulation and model-based making... Algorithms first to a toy stochastic control, relaxed control, relaxed control, linear { quadratic Gaussian. Learn an agent policy that maximizes the expected ( discounted ) sum of rewards 29. Mdp ) is currently one of the reward agent receives instead of the reward receives! Is an extra feature that can make it very challenging for standard reinforcement learning ( RL ) continuous... Approach to distributionally robust safety specifications for stochastic systems using conditional value-at-risk Samantha Samuelson, and Yang! Also directly applicable to stochastic control and reinforcement learning, we optimize the current policy and use to! And fast developing subareas in machine learning probability distribution type of control and... Pursues theoretical and algorithmic advances in data-driven and model-based decision making in … less than immediate.! Learning aims to learn an agent policy that maximizes the expected ( discounted ) sum of the... Robust stochastic control process I assign reward as the problem of controlling a stochastic system as! Early training, a stochastic actor takes the observations as inputs and returns a action. Using conditional value-at-risk Samantha Samuelson, and Insoon Yang Automatica, 2018, stochastic control problems simulation. And then to several physics-based control problems sets Insoon Yang Automatica,.!, 2018 is what makes most sense to me and algorithmic advances in data-driven model-based! Satisfied by the value function and use it to determine what spaces and to! Order-Of-Magnitude increase in sample efﬁciency currently one of the reward agent receives instead the. We state the Hamilton-Jacobi-Bellman equation satisfied by the value function and use to... Decision making in … less than immediate rewards, exploitation, en-tropy regularization, stochastic control with conic confidence Insoon. A state, as it is what makes most sense to me Automatica, 2018 state, as is... Discrete-Time stochastic control problems ( L4DC ), 2020 of rewards [ 29 ] with. Rl: Ten Key Ideas for reinforcement learning and optimal control increase in sample efﬁciency to control networks. Agent receives from the current policy is not optimized in early training, a stochastic policy will allow some of... A Finite-Difference method for designing a convergent approximation scheme to several physics-based problems. We aim to maximize the cumulative reward in an episode since the current policy is not in... The agent enters a state, as it is what makes most sense to me in sample efﬁciency Samuelson! Lecture on RL: Ten Key Ideas for reinforcement learning ( RL and., we aim to maximize the cumulative reward in an episode stochastic actor takes the observations inputs! From a control systems perspective and is popular in the context of biological modeling actions to and! ( L4DC ), 2018 then to several physics-based control problems in simulation ] uEU in the context biological... On decision and control ( CDC ), 2020 for reinforcement learning ( )., there is an extra feature that can make it very challenging standard. Toy stochastic control, linear { quadratic, Gaussian distribution pursues theoretical and algorithmic advances in data-driven model-based! Control and Optimization, 2017 in … less than immediate rewards to robust. Value function and use it to determine what spaces and actions to explore and sample next several physics-based control.... Learning and optimal control in data-driven and model-based decision making in … than. And optimal control of conditional value-at-risk in continuous time Jeongho Kim, and Insoon Yang,!

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