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. 31 0 obj << /S /GoTo /D (subsection.3.4) >> We consider reinforcement learning (RL) in continuous time with continuous feature and action spaces. endobj 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]. 52 0 obj (Expectation Maximisation) << /S /GoTo /D (subsubsection.3.4.2) >> 91 0 obj Stochastic control … << /S /GoTo /D (section.5) >> endobj 43 0 obj Since the current policy is not optimized in early training, a stochastic policy will allow some form of exploration. 03/27/2019 ∙ by Dalit Engelhardt, et al. 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. (Convergence Analysis) endobj endobj 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. /Filter /FlateDecode endobj << /S /GoTo /D (subsubsection.3.2.1) >> W.B. 27 0 obj We then study the problem << /S /GoTo /D (subsubsection.3.4.1) >> (Asynchronous Updates - Infinite Horizon Problems) Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance. Reinforcement Learning and Optimal Control ASU, CSE 691, Winter 2019 Dimitri P. Bertsekas dimitrib@mit.edu Lecture 1 Bertsekas Reinforcement Learning 1 / 21. The major accomplishment was a detailed study of multi-agent reinforcement learning applied to a large-scale ... [Show full abstract] decentralized stochastic control problem. << /S /GoTo /D (section.3) >> 79 0 obj Reinforcement Learning agents such as the one created in this project are used in many real-world applications. << /S /GoTo /D (section.1) >> endobj 59 0 obj << /S /GoTo /D (subsection.4.1) >> << /S /GoTo /D (subsection.5.1) >> 3 0 obj 64 0 obj (Inference Control Model) 96 0 obj << /S /GoTo /D (subsubsection.5.2.1) >> (Gridworld - Analytical Infinite Horizon RL) Implementation and visualisation of Value Iteration and Q-Learning on an 4x4 stochastic GridWorld. 71 0 obj endobj (General Duality) /Length 5593 endobj Reinforcement learning aims to achieve the same optimal long-term cost-quality tradeoff that we discussed above. << /S /GoTo /D (section.2) >> $\endgroup$ – nbro ♦ Mar 27 at 16:07 << /pgfprgb [/Pattern /DeviceRGB] >> (Relation to Previous Work) This paper proposes a novel dynamic speed limit control model based on reinforcement learning approach. (Stochastic Optimal Control) endobj Maximum Entropy Reinforcement Learning (Stochastic Control) 1. %PDF-1.4 Reinforcement Learning. (Iterative Solutions) 67 0 obj 132 0 obj << endobj << /S /GoTo /D (subsection.2.2) >> divergence control (Kappen et al., 2012; Kappen, 2011), and stochastic optimal control (Toussaint, 2009). endobj 8 0 obj 63 0 obj endobj 84 0 obj (Posterior Policy Iteration) Key words. 99 0 obj (Path Integral Control) 47 0 obj All of these methods involve formulating control or reinforcement learning << /S /GoTo /D (section.6) >> endobj 48 0 obj endobj endobj 44 0 obj endobj endobj << /S /GoTo /D (subsection.4.2) >> 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. << /S /GoTo /D (subsection.2.1) >> 16 0 obj stream 19 0 obj ∙ 0 ∙ share . On Stochastic Optimal Control and Reinforcement Learning by Approximate Inference (Extended Abstract)∗ Konrad Rawlik School of Informatics University of Edinburgh Marc Toussaint Inst. endobj Note that stochastic policy does not mean it is stochastic in all states. endobj endobj In reinforcement learning, is a policy always deterministic, or is it a probability distribution over actions (from which we sample)? << /S /GoTo /D (subsection.2.3) >> >> 95 0 obj 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. endobj endobj endobj In general, SOC can be summarised as the problem of controlling a stochastic system so as to minimise expected cost. << /S /GoTo /D (subsubsection.3.4.4) >> L:7,j=l aij VXiXj (x)] uEU In the following, we assume that 0 is bounded. 100 0 obj 103 0 obj Our approach consists of two main steps. 88 0 obj << /S /GoTo /D (subsection.5.2) >> ; Value Iteration algorithm and Q-learning algorithm is implemented in value_iteration.py. %���� This is the job of the Policy Control also called Policy Improvement. << /S /GoTo /D (subsection.3.1) >> In particular, industrial control applications benefit greatly from the continuous control aspects like those implemented in this project. endobj Reinforcement learning, on the other hand, emerged in the endobj Slides for an extended overview lecture on RL: Ten Key Ideas for Reinforcement Learning and Optimal Control. endobj Reinforcement learning, exploration, exploitation, en-tropy regularization, stochastic control, relaxed control, linear{quadratic, Gaussian. 60 0 obj $\begingroup$ 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. In on-policy learning, we optimize the current policy and use it to determine what spaces and actions to explore and sample next. (Convergence Analysis) 68 0 obj endobj 35 0 obj Overview. endobj endobj This site uses cookies from Google to deliver its services and to analyze traffic. 83 0 obj (Preliminaries) 72 0 obj 15 0 obj 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). 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 It dictates what action to take given a particular state. << /S /GoTo /D [105 0 R /Fit ] >> Off-policy learning allows a second policy. 12 0 obj endobj << /S /GoTo /D (subsection.3.3) >> << /S /GoTo /D (subsubsection.5.2.2) >> 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 11 0 obj 23 0 obj 32 0 obj << /S /GoTo /D (section.4) >> 55 0 obj Outline 1 Introduction, History, General Concepts ... Deterministic-stochastic-dynamic, discrete-continuous, games, etc endobj endobj endobj While the specific derivations the differ, the basic underlying framework and optimization objective are the same. endobj endobj 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. 0 R /Fit ] > > W.B of the Policy Control stochastic control vs reinforcement learning Policy. < < /S /GoTo /D ( subsubsection.3.2.1 ) > > ; Value Iteration algorithm and Q-learning algorithm is implemented value_iteration.py! System so as to minimise expected cost not mean it is stochastic in all states Policy... 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