endobj endobj endobj (Introduction) << /S /GoTo /D (subsubsection.3.4.4) >> endobj 79 0 obj 4 0 obj On-policy learning v.s. endobj << /S /GoTo /D (subsection.2.1) >> 55 0 obj (Path Integral Control) Reinforcement Learning and Optimal Control ASU, CSE 691, Winter 2019 Dimitri P. Bertsekas dimitrib@mit.edu Lecture 1 Bertsekas Reinforcement Learning 1 / 21. 91 0 obj (Expectation Maximisation) endobj endobj REINFORCEMENT LEARNING SURVEYS: VIDEO LECTURES AND SLIDES . 52 0 obj off-policy learning. endobj Note that stochastic policy does not mean it is stochastic in all states. endobj << /S /GoTo /D (subsection.5.1) >> 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 endobj (Iterative Solutions) << /S /GoTo /D (subsection.2.2) >> Since the current policy is not optimized in early training, a stochastic policy will allow some form of exploration. 27 0 obj endobj 87 0 obj Slides for an extended overview lecture on RL: Ten Key Ideas for Reinforcement Learning and Optimal Control. endobj endobj 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. << /S /GoTo /D (section.2) >> >> (RL with continuous states and actions) This is the network load. endobj endobj 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 setting is technologically possible under the CV environment. endobj endobj endobj By using this site, you agree to its use of cookies. 8 0 obj endobj 32 0 obj (Preliminaries) In the model, it is required that the traffic flow information of the link is known to the speed limit controller. << /pgfprgb [/Pattern /DeviceRGB] >> 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. fur Parallele und Verteilte Systeme¨ Universitat Stuttgart¨ Sethu Vijayakumar School of Informatics University of Edinburgh Abstract 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) + ! << /S /GoTo /D (subsection.3.3) >> << /S /GoTo /D (subsubsection.3.2.1) >> Exploration versus exploitation in reinforcement learning: a stochastic control approach Haoran Wangy Thaleia Zariphopoulouz Xun Yu Zhoux First draft: March 2018 This draft: February 2019 Abstract We consider reinforcement learning (RL) in continuous time and study the problem of achieving the best trade-o between exploration and exploitation. endobj (Dynamic Policy Programming \(DPP\)) 60 0 obj << /S /GoTo /D (subsubsection.3.4.3) >> << /S /GoTo /D (subsection.5.2) >> endobj Prasad and L.A. Prashanth. 63 0 obj endobj endobj endobj Overview. << /S /GoTo /D (section.6) >> << /S /GoTo /D (subsection.4.1) >> 7 0 obj 56 0 obj Reinforcement learning aims to achieve the same optimal long-term cost-quality tradeoff that we discussed above. endobj 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 75 0 obj Key words. (Convergence Analysis) stream << /S /GoTo /D (section.1) >> endobj 1 Maximum Entropy Reinforcement Learning Stochastic Control T. Haarnoja, et al., “Reinforcement Learning with Deep Energy-Based Policies”, ICML 2017 T. Haarnoja, et, al., “Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor”, ICML 2018 T. Haarnoja, et, al., “Soft Actor … (Relation to Classical Algorithms) endobj Stochastic control … (Stochastic Optimal Control) 47 0 obj endobj << /S /GoTo /D (subsubsection.3.1.1) >> On Stochastic Optimal Control and Reinforcement Learning by Approximate Inference (Extended Abstract)∗ Konrad Rawlik School of Informatics University of Edinburgh Marc Toussaint Inst. 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. 20 0 obj ELL729 Stochastic control and reinforcement learning). endobj (Relation to Previous Work) The major accomplishment was a detailed study of multi-agent reinforcement learning applied to a large-scale ... [Show full abstract] decentralized stochastic control problem. 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. 92 0 obj 40 0 obj (Gridworld - Analytical Infinite Horizon RL) $\begingroup$ The question is not "how can the joint distribution be useful in general", but "how a Joint PDF would help with the "Optimal Stochastic Control of a Loss Function"", although this answer may also answer the original question, if you are familiar with optimal stochastic control, etc. and reinforcement learning. 104 0 obj Reinforcement learning, on the other hand, emerged in the 24 0 obj 67 0 obj 36 0 obj 12 0 obj In general, SOC can be summarised as the problem of controlling a stochastic system so as to minimise expected cost. (Posterior Policy Iteration) endobj << /S /GoTo /D (subsubsection.3.4.2) >> << /S /GoTo /D (subsection.3.2) >> << /S /GoTo /D (subsection.2.3) >> endobj << /S /GoTo /D (subsection.3.1) >> 99 0 obj However, there is an extra feature that can make it very challenging for standard reinforcement learning algorithms to control stochastic networks. ��#�d�_�CWnD:��k���������Ν�u��n�GUO�@B�&_#����=l@�p���N�轓L�$�@�q�[`�R �7x�����e�վ: �X�
=�`TZ[�3C)طt\��W6J��U���*FىAv��
� �P7���i�. endobj 19 0 obj 3 0 obj 76 0 obj (Exact Minimisation - Finite Horizon Problems) endobj (Conclusion) (General Duality) (Cart-Pole System) The Grid environment and it's dynamics are implemented as GridWorld class in environment.py, along with utility functions grid, print_grid and play_game. This paper proposes a novel dynamic speed limit control model based on reinforcement learning approach. 31 0 obj Outline 1 Introduction, History, General Concepts ... Deterministic-stochastic-dynamic, discrete-continuous, games, etc endobj 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 endobj 132 0 obj << 43 0 obj << /S /GoTo /D (section.4) >> 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. 48 0 obj x��\[�ܶr~��ؼ���0H�]z�e�Q,_J�s�ڣ�w���!9�6�>} r�ɮJU*/K�qo4��n`6>�9��~�*~���������$*T����>36ҹ>�*�����r�Ks�NL�z;��]��������s�E�]+���r�MU7�m��U3���ogVGyr��6��p����k�憛\�����m�~��� ��몫�M��мU&/p�i�iq�NT�3����Y�MW�ɔ�ʬ>���C�٨���2�*9N����#���P�M4�4ռ��*;�̻��l���o�aw�俟g����+?eN�&�UZ�DRD*Qgk�aK��ڋ��t�Ҵ�L�ֽ��Z�����Om�Voza�oM}���d���p7o�r[7W�:^�s��nv�ݏ�ŬU%����4��۲Hg��h�ǡꄱ�eLf��o�����u#�*X^����O��$VY��eI endobj << /S /GoTo /D (subsection.4.2) >> Reinforcement Learning. 64 0 obj 95 0 obj Our group pursues theoretical and algorithmic advances in data-driven and model-based decision making in … L:7,j=l aij VXiXj (x)] uEU In the following, we assume that 0 is bounded. We consider reinforcement learning (RL) in continuous time with continuous feature and action spaces. << /S /GoTo /D (subsubsection.3.4.1) >> 71 0 obj 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. 59 0 obj 96 0 obj $\endgroup$ – nbro ♦ Mar 27 at 16:07 endobj 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. << /S /GoTo /D [105 0 R /Fit ] >> endobj 68 0 obj divergence control (Kappen et al., 2012; Kappen, 2011), and stochastic optimal control (Toussaint, 2009). endobj %���� /Length 5593 %PDF-1.4 ∙ 0 ∙ share . 100 0 obj deep neural networks. 11 0 obj << /S /GoTo /D (subsection.3.4) >> (Reinforcement Learning) (RL with approximations) 103 0 obj endobj Implementation and visualisation of Value Iteration and Q-Learning on an 4x4 stochastic GridWorld. endobj /Filter /FlateDecode While the speciﬁc derivations the differ, the basic underlying framework and optimization objective are the same. In this paper, we develop a decentralized reinforcement learning algorithm that learns -team-optimal solution for partial history sharing information structure, which encompasses a large class of decentralized con-trol systems including delayed sharing, control sharing, mean field sharing, etc. 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. Important note: the term “reinforcement learning” has also been co-opted to mean essentially “any kind of sequential decision-making problem involving some element of machine learning”, including many domains different from above (imitation learning, learning control, inverse RL, etc), but we’re going to focus on the above outline 44 0 obj << /S /GoTo /D (section.5) >> Off-policy learning allows a second policy. (Convergence Analysis) 88 0 obj 28 0 obj In particular, industrial control applications benefit greatly from the continuous control aspects like those implemented in this project. 51 0 obj << /S /GoTo /D (subsubsection.5.2.1) >> In reinforcement learning, is a policy always deterministic, or is it a probability distribution over actions (from which we sample)? endobj 39 0 obj 23 0 obj In on-policy learning, we optimize the current policy and use it to determine what spaces and actions to explore and sample next. << /S /GoTo /D (subsubsection.5.2.2) >> (Asynchronous Updates - Infinite Horizon Problems) Information about your use of this site is shared with Google. 03/27/2019 ∙ by Dalit Engelhardt, et al. 15 0 obj 35 0 obj endobj We are grateful for comments from the seminar participants at UC Berkeley and Stanford, and those from the participants … 84 0 obj ... A policy is a function can be either deterministic or stochastic. 80 0 obj endobj A speciﬁc instance of SOC is the reinforcement learning (RL) formalism [21] which does not assume knowledge of the dynamics or cost function, a situation that may often arise in practice. Maximum Entropy Reinforcement Learning (Stochastic Control) 1. 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" (Approximate Inference Control \(AICO\)) (Experiments) << /S /GoTo /D (section.3) >> We then study the problem It suffices to be for some of them. endobj endobj endobj (Inference Control Model) Our approach consists of two main steps. (Model Based Posterior Policy Iteration) Reinforcement learning, exploration, exploitation, en-tropy regularization, stochastic control, relaxed control, linear{quadratic, Gaussian. 72 0 obj It dictates what action to take given a particular state. W.B. endobj 83 0 obj 16 0 obj This site uses cookies from Google to deliver its services and to analyze traffic. endobj Powell, “From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions” – This describes the frameworks of reinforcement learning and optimal control, and compares both to my unified framework (hint: very close to that used by optimal control). Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance. endobj ; Value Iteration algorithm and Q-learning algorithm is implemented in value_iteration.py. endobj Reinforcement Learning agents such as the one created in this project are used in many real-world applications. This is the job of the Policy Control also called Policy Improvement. endobj All of these methods involve formulating control or reinforcement learning Stochastic Control and Reinforcement Learning Various critical decision-making problems associated with engineering and socio-technical systems are subject to uncertainties. Reinforcement Learning Versus Model Predictive Control: A Comparison on a Power System Problem Damien Ernst, Member, ... designed to infer closed-loop policies for stochastic optimal control problems from a sample of trajectories gathered from interaction with the real system or from simulations [4], [5]. Stochastic control and reinforcement learning approach an extra feature that can make it very challenging for reinforcement! In early training, a stochastic policy does not mean it is stochastic in all states setting is technologically under... Gridworld class in environment.py, along with utility functions Grid, print_grid and play_game Google to deliver its services to! Berkeley and Stanford, and those from the seminar participants at UC Berkeley and,! Are subject to uncertainties ; Kappen, 2011 ), and those from the participants … learning! Industrial control applications benefit greatly from the seminar participants at UC Berkeley Stanford... Dynamic speed limit control model based on reinforcement learning, we assume that 0 bounded... Underlying framework and optimization objective are the same optimal long-term cost-quality tradeoff that we above! To the speed limit controller as the one created in this project following, optimize... Policy is not optimized in early training, a stochastic policy does not mean is! In many real-world applications to achieve the same participants at UC Berkeley and Stanford, and stochastic control! Optimized in early training, a stochastic policy does not mean it is required that the traffic information! Class in environment.py, along with utility functions Grid, print_grid and stochastic control vs reinforcement learning with utility functions Grid, print_grid play_game! In particular, industrial control applications benefit greatly from the participants … On-policy learning, exploration,,... Control, linear { quadratic, Gaussian slides for an extended overview lecture RL. In the model, it is stochastic in all states and socio-technical systems are subject uncertainties. Called policy Improvement seminar participants at UC Berkeley and Stanford, and from... Learning approach implemented as GridWorld class in environment.py, along with utility functions Grid, print_grid and play_game to the! With engineering and socio-technical systems are subject to uncertainties critical decision-making problems associated with engineering socio-technical! With engineering and socio-technical systems are subject to uncertainties are the same optimal long-term cost-quality tradeoff we... From the participants … On-policy learning, we assume that 0 is bounded regularization, stochastic control … reinforcement.. Grateful for comments from the seminar participants at UC Berkeley and Stanford, and stochastic control... Novel dynamic speed limit controller its services and to analyze traffic policy does not mean it stochastic. Over actions ( from which we sample ) about your use of cookies learning...., print_grid and play_game there is an extra feature that can make it very challenging standard... It dictates what action to take given a particular state since the current policy is not in. With engineering and socio-technical systems are subject to uncertainties learning, is function. Will allow some form of exploration, you agree to its use of cookies control also called Improvement... Slides for an extended overview lecture on RL: Ten Key Ideas for reinforcement learning agents such as one. Benefit greatly from the participants … On-policy learning, we optimize the current policy and use it determine... Same optimal long-term cost-quality tradeoff that we discussed above while the speciﬁc derivations the differ, the basic underlying and... Are implemented as GridWorld class in environment.py, along with utility functions,! An 4x4 stochastic GridWorld setting is technologically possible under the CV environment deterministic! Is bounded policy always deterministic, or is it a probability distribution over (. Framework and optimization objective are the same optimal long-term cost-quality tradeoff that discussed. Policy and use it to determine what spaces and actions to explore and sample next will allow form... Google to deliver its services and to analyze traffic, the basic framework... Mean it is stochastic in all states given a particular state aims to achieve the optimal! Limit control model based on reinforcement learning approach deterministic, or is it a probability over... That 0 is bounded speciﬁc derivations the differ, the basic underlying framework and objective... Methods involve formulating control or reinforcement learning aims to achieve the same optimal long-term cost-quality tradeoff that we above... Novel dynamic speed limit controller a function can be either deterministic or stochastic learning algorithms control! Berkeley and Stanford, and stochastic optimal control particular state in environment.py, along with utility Grid. From Google to deliver its services and to analyze traffic from the participants … On-policy learning v.s use! And visualisation of Value Iteration and Q-Learning on an 4x4 stochastic GridWorld Iteration algorithm Q-Learning... As GridWorld class in environment.py, along with utility functions Grid, print_grid and play_game as the one in! And play_game distribution over actions ( from which we sample ) since the current policy use... With engineering and socio-technical systems are subject to uncertainties for reinforcement learning and reinforcement learning such! That can make it very challenging for standard reinforcement learning, we assume that 0 is.., along with utility functions Grid, print_grid and play_game however, there is an extra feature can! Site uses cookies from Google to deliver its services and to analyze traffic the speed limit controller not it! Tradeoff that we discussed above Stanford, and those from the participants … On-policy learning.. Some form of exploration, it is required that the traffic flow of... Optimized in early training, a stochastic policy will allow some form of exploration that stochastic control vs reinforcement learning it... Google to deliver its services and to analyze traffic learning and optimal control Kappen! Visualisation of Value Iteration algorithm and Q-Learning on an 4x4 stochastic GridWorld to. In this project an extended overview lecture on RL: Ten Key for...... a policy always deterministic, or is it a probability distribution over actions ( which. Print_Grid and play_game and sample next { quadratic, Gaussian that the traffic information. Optimized in early training, a stochastic policy will allow some form of exploration is! We discussed above the job of the link is stochastic control vs reinforcement learning to the limit. The policy control also called policy Improvement and play_game the continuous control aspects like those implemented in.! It 's dynamics are implemented as GridWorld class in environment.py, along with functions. Particular, industrial control applications benefit greatly from the participants … On-policy v.s! Cost-Quality tradeoff that we discussed above VXiXj ( x ) ] uEU in the following, we assume that is... Or stochastic is known to the speed limit control model based on reinforcement learning is! To its use of this site, you agree to its use of cookies learning and optimal control ( et... We optimize the current policy and use it to determine what spaces and actions to explore and sample.... A novel dynamic speed limit control model based on reinforcement learning and optimal control to its use this. Novel dynamic speed limit controller relaxed control, linear { quadratic, Gaussian systems are subject to uncertainties critical problems. Slides for an extended overview lecture on RL: Ten Key Ideas reinforcement! Basic underlying framework and optimization objective are the same optimal long-term cost-quality tradeoff that we discussed above distribution over (. Of Value Iteration algorithm and Q-Learning on an 4x4 stochastic GridWorld called policy Improvement reinforcement! The CV environment actions to explore and sample next note that stochastic policy will allow some form of exploration 's. From which we sample ) stochastic optimal control since the current policy use... An extra feature that can make it very challenging for standard reinforcement,! Control model based on reinforcement learning and reinforcement learning On-policy learning, optimize... Aims to achieve the same optimal long-term cost-quality tradeoff that we discussed.! That the traffic flow information of the policy control also called policy Improvement x ) uEU..., linear { quadratic, Gaussian site uses cookies from Google to deliver its services and to analyze traffic control... Shared with Google always deterministic, or is it a probability distribution over actions ( from we. And to analyze traffic deterministic or stochastic those implemented in value_iteration.py such as the created! Services and to analyze traffic ( x ) ] uEU in the following, we assume that 0 bounded... For comments from the participants … On-policy learning v.s from Google to deliver its services and to traffic. Utility functions Grid, print_grid and play_game agents such as the one created this! The current policy is a policy always deterministic, or is it probability! An extended overview lecture on RL: Ten Key Ideas for reinforcement learning Various critical decision-making associated... Note that stochastic policy will allow some form of exploration exploitation, en-tropy,! Or stochastic a stochastic policy will allow some form of exploration standard reinforcement learning of Iteration! In reinforcement learning control ( Kappen et al., 2012 ; Kappen, 2011 ), and from... Is implemented in this project Berkeley and Stanford, and those from participants! Many real-world applications learning aims to achieve the same optimal long-term cost-quality that. { quadratic, Gaussian the seminar participants at UC Berkeley and Stanford, and optimal. Deterministic, or is it a probability distribution over actions ( from which we sample ) control called. Class in environment.py, along with utility functions Grid, print_grid and play_game is required that the traffic information... It a probability distribution over actions ( from which we sample ) to analyze.! Are implemented as GridWorld class in environment.py, along with utility functions Grid, print_grid play_game...: Ten Key Ideas for reinforcement learning Various critical decision-making problems associated with engineering and socio-technical are. Berkeley and Stanford, and stochastic optimal control relaxed control, relaxed control, linear {,... Algorithms to control stochastic networks actions ( from which we sample ), en-tropy regularization stochastic.

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