2 3 Markov Decision Process r k+1 s k+1 Environment Environment - PowerPoint PPT Presentation

Markov Decision Process r k+1 s k+1 Environment Environment Action a k State s k Reward r k Agent 4

r k+1 s k+1 Environment Action a k Reward r k Critic Value Function State s k TD Error Policy Actor Agent 10

L. Busoniu, R. Babuska, and B. De Schutter, “A comprehensive survey of multiagent reinforcement learning,” IEEE Trans. Systems, Man and Cybernetics-Part C: Applications and Reviews, vol. 38, no.2, Mar. 2008. 15

L. Busoniu, R. Babuska, and B. De Schutter, “A comprehensive survey of multiagent reinforcement learning,” IEEE Trans. Systems, Man and Cybernetics-Part C: Applications and Reviews, vol. 38, no.2, Mar. 2008. Temporal- Game difference Theory RL Direct Policy Search 18

L. Busoniu, R. Babuska, and B. De Schutter, “A comprehensive survey of multiagent reinforcement learning,” IEEE Trans. Systems, Man and Cybernetics-Part C: Applications and Reviews, vol. 38, no.2, Mar. 2008. Task Type -> Cooperative Competitive Mixed Agent Awareness Independent Coordination-free Opponent- Agent-independent independent Tracking Coordination-based - Agent-tracking Aware Indirect Opponent-aware Agent-aware coordination 19

L. Busoniu, R. Babuska, and B. De Schutter, “A comprehensive survey of multiagent reinforcement learning,” IEEE Trans. Systems, Man and Cybernetics-Part C: Applications and Reviews, vol. 38, no.2, Mar. 2008. Obstacle S 1 S 2 L 1 R 1 L 2 R 2 1 2 Q L 2 S 2 R 2 L 1 10 -5 0 S 1 -5 -10 -5 R 1 -10 -5 10 21

C. Guestrin, M. Lagoudakis, and R. Parr, “Coordinated reinforcement learning,” in Proc. Int’l Conf. Machine Learning (ICML-02), Jul. 2002. 1 Q 1 Q 3 f 4 2 3 Q 2 Q 4 4 22

C. Guestrin, M. Lagoudakis, and R. Parr, “Coordinated reinforcement learning,” in Proc. Int’l Conf. Machine Learning (ICML-02), Jul. 2002. 1 Q 1 Q 3 f 4 2 3 Q 2 Q 4 4 23

L. Busoniu, R. Babuska, and B. De Schutter, “A comprehensive survey of multiagent reinforcement learning,” IEEE Trans. Systems, Man and Cybernetics-Part C: Applications and Reviews, vol. 38, no.2, Mar. 2008. L 1 R 1 L 2 R 2 1 2 Q 1 L 2 R 2 Q 2 L 2 R 2 L 1 0 1 L 1 0 -1 R 1 -10 10 R 1 10 -10 25

L. M. Littman, “Markov games as a framework for multi-agent reinforcement learning,” in Proc. Int’l Conf. Machine Learning (ICML-94 ), Jul. 1994. 26

L. Busoniu, R. Babuska, and B. De Schutter, “A comprehensive survey of multiagent reinforcement learning,” IEEE Trans. Systems, Man and Cybernetics-Part C: Applications and Reviews, vol. 38, no.2, Mar. 2008. Q 1 L 2 R 2 L 1 0 3 L 1 R 1 1 Left R 1 2 0 Right Room Room L 2 R 2 Q 2 L 2 R 2 2 L 1 0 2 R 1 3 0 28

2 3 Markov Decision Process r k+1 s k+1 Environment Environment - PowerPoint PPT Presentation

2 3 Markov Decision Process r k+1 s k+1 Environment Environment Action a k State s k Reward r k Agent 4 5 6 7 8 9 r k+1 s k+1 Environment Action a k Reward r k Critic Value Function State s k TD Error Policy Actor Agent 10 11 12

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Markov Decision Process Assumption: agent gets to observe the state [Drawing from Sutton and

Markov Decision Process Assumption: agent gets to observe the state [Drawing from Sutton and

Markov Decision Process Assumption: agent gets to observe the state [Drawing from Sutton and

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2 3 Markov Decision Process r k+1 s k+1 Environment Environment - PowerPoint PPT Presentation

2 3 Markov Decision Process r k+1 s k+1 Environment Environment Action a k State s k Reward r k Agent 4 5 6 7 8 9 r k+1 s k+1 Environment Action a k Reward r k Critic Value Function State s k TD Error Policy Actor Agent 10 11 12

Does the Markov decision process fit the data Testing for the Markov property in sequential

POMDPs (Ch. 17.4-17.6) Markov Decision Process Recap of Markov Decision Processes (MDPs): Know:

Processes (MDP) Prof. Kuan-Ting Lai 2020/3/20 Markov Decision Process (MDP)

Markov Decision Process Assumption: agent gets to observe the state [Drawing from Sutton and

Markov Decision Process Assumption: agent gets to observe the state [Drawing from Sutton and

Markov Decision Process Assumption: agent gets to observe the state [Drawing from Sutton and

Markov Systems, Markov Decision Processes, and Dynamic Programming Andrew W. Moore Note to

Outline Md Md Markov Markov Decision Decision Processes Processes Grid World Example

Markov Decision Process AssumpCon: agent gets to observe the

Planning and Optimization F1. Markov Decision Processes Malte Helmert and Thomas Keller

Markov Decision Processes Mausam CSE 515 Operations Research Machine Graph Learning Theory

Markov decision processes and interval Markov chains: exploiting the connection Mingmei Teo

Markov Decision Processes [RN2] Sec 17.1, 17.2, 17.4, 17.5 [RN3] Sec 17.1, 17.2, 17.4 CS 486/686

Solving Continuous MDPs with Discretization Pieter Abbeel UC Berkeley EECS Markov Decision

Markov Decision Processes and Exact Solution Methods: Value Iteration Policy Iteration Linear

1 Markov Decision Processes Markov Decision Processes An MDP is defined by: An MDP is

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Code Injection From the Hypervisor: Removing the need for in-guest agents Matt Conover &amp;

3. Reasoning in Agents Part 2: BDI Agents ems (SMA-UPC) Javier Vzquez-Salceda q Multiagent

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INTELLIGENT SYSTEMS OVER THE INTERNET Web-Bas Based ed Intellige ligent nt Syst stems

July 2017 Webinar Agenda Lindsey 2018 Certification and Training NEW Consumer Connector

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Code Injection From the Hypervisor: Removing the need for in-guest agents Matt Conover &