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Sequential Decision Making under Uncertainty

Reading group (2007)

Past meetings:
Date Topic Presenter Material
Monday November 26, at 16:00 Decentralized POMDPs Matthijs TBD
Monday October 29, at 16:00 Reinforcement Learning on a Stochastic Timed Discrete-Event system Gonšalo Neto paper
Monday October 15, at 16:00 Policy gradient methods Francisco TBD
Monday October 1, at 17:00 Active perception using POMDPs Matthijs slides
Monday June 18, at 17:00 Non-Markovian reinforcement functions Valdinei Silva Thiebaux et al., JAIR 2006
Monday June 4, at 17:00 Bayesian Reinforcement Learning Luis Montesano Poupart et al., ICML 2006
Monday May 7, at 17:00 Reinforcement learning in general state spaces Francisco slides
Monday April 16, at 17:00 Constrained MDPs Nelson Gonçalves slides
Monday March 26, at 17:00 Multiagent models for partially observable environments Matthijs slides
Monday March 12, at 17:00 Multiagent learning Gonçalo Neto slides, survey by Gonçalo
Monday February 26, at 17:00 Predictive State Representations Rodrigo Ventura slides, paper, web page
Monday February 12, at 17:00 Partially observable Markov decision processes Matthijs slides
Monday February 5, at 17:00 Reinforcement learning Francisco slides, Q-learning convergence proof
Monday January 22, at 17:00 Markov Decision Processes Matthijs slides
Thursday January 11, at 10:00 Markov Chains Francisco slides
Thursday January 4, at 10:00 Introduction Matthijs / Francisco slides

Schedule: bi-weekly on Mondays at 16:00.
Location: ISR meeting room, 7th floor, Torre Norte.
Organisers: Francisco Melo and Matthijs Spaan
Announcements are sent to the mailing list

Goal
What is this all about?
List of potential topics

Goal

The purpose of this reading group is to gather on a regular basis a group of people with interests in sequential decision making under uncertainty. During our meetings, we introduce standard references on the different topics and discuss state-of-the-art research. We encourage people to suggest additional topics to those listed below, as well as to come forward and present their own research on the different areas.

What is this all about?

And what do we mean by sequential decision making under uncertainty? Consider the following scenario.

Maze
An intelligent mouse must move about in the maze so as to get the cheese as quickly as possible. At each square, it must choose whether to move up, down, left or right. There is some uncertainty in the outcome of the actions of the mouse. For example, it may happen that, sometimes, the action "Move left" the mouse two squares to the left instead of just one. On the other hand, the mouse must try to realize in which square of the maze it is by observing its surroundings. Of course, there are many squares that look alike, and so the mouse isn't always able to tell exactly where it is...

This is a typical single agent sequential decision problem. The agent (mouse) must sequentially choose an action among its possible actions (left, right, up, down) so as to optimize some criterion (in this case, reach the cheese as quickly as possible). The actions usually have uncertain outcome and there is some perceptual aliasing involved (the mouse cannot always accurately perceive its position on the maze).

A common model to address a situation as the one above is called partially observable Markov decision process. In our meetings, we discuss this and other models used to address this kind of problems, as well as classical and state-of-the-art solution techniques (i.e., methods used to determine the "best" choice of actions). We will also address problems with multiple agents (as an example, consider our mouse scenario once again, and suppose that there are several mice, each trying to be the first one to reach the cheese).

A more detailed list of possible topics addressed in our meetings can be found below.

Some topics addressed

Within the topic of sequential decision making several subtopics can be addressed:
  • Markov chains: main properties; usefulness as a basic building block of more complex models (MDPs, Markov games, POMDPs, etc.);
  • Stochastic approximation algorithms: basic algorithms, convergence, applications in RL;
  • Markov decision processes: notation, properties and standard solution techniques (VI, PI, etc);
  • Reinforcement learning: standard algorithms such as TD(lambda), SARSA, Q-learning, actor-critic, etc.
  • Advanced RL algorithms: DYNA, E3, prioritized sweeping, parti-game, RTDP, hierarchical RL, Bayesian Q-learning, etc.;
  • Generalization in RL: TD(lambda) with linear function approximation, interpolation-based Q-learning, Q-learning with soft-state aggregation, Baird's residual algorithms, kernel-based RL, LSTD, etc.;
  • Implementation issues: Approximation architectures (tile-coding, kernel-based, convex interpolation), optimistic initialization, exploration/exploitation issues, eligibility traces;
  • Partially observable Markov decision processes: main properties and standard solution methods (Sondik's two-pass, Chen's linear support, witness, incremental pruning, etc.);
  • Approximate solutions to POMDPs: Heuristic-based (Q-MDP, entropy-based, etc.), Matthijs' sampling-based VI, Baxter's gradient descent methods, reinforcement learning methods.
  • Predictive state representations: main idea, applications and algorithms;
  • Markov games: notation, properties and game theoretic approaches;
  • Multi-agent reinforcement learning: Minimax-Q, FF-Q, Nash-Q, multi-agent DYNA-Q, correlated-Q, etc.
  • Coordination: Fictitious play, coordination graphs, social conventions, adaptive play, etc.
  • Partial observability in multi-agent problems: POSGs, Dec-POMDPs, I-POMDPs;
  • Solution methods for POSGs: Hansen's DP, Montemerlo's BaGA, etc.