models: reinforcement learning & fMRI

Apr 07, 2019

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models: reinforcement learning & fMRI. Nathaniel Daw 11/28/2007. overview. reinforcement learning model fitting: behavior model fitting: fMRI. overview. reinforcement learning simple example tracking choice model fitting: behavior model fitting: fMRI.

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models: reinforcement learning & fMRI

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Make your work visible in popular websites, Wikipedia, YouTube, FaceBook, MySpace, Orkut, etc. There are lots of free tools on the web, use them! ...

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Reinforcement Learning

Reinforcement Learning. Introduction Passive Reinforcement Learning Temporal Difference Learning Active Reinforcement Learning Applications Summary. Introduction. Supervised Learning:. Example Class. Reinforcement Learning:. …. Situation Reward. Situation Reward.

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Reinforcement Learning

Reinforcement Learning. Lisa Torrey University of Wisconsin – Madison HAMLET 2009. Outline. Reinforcement learning What is it and why is it important in machine learning? What machine learning algorithms exist for it? Q-learning in theory How does it work? How can it be improved?

632 views • 36 slides

Reinforcement Learning

Reinforcement Learning. Zohreh Raziei. Spring 2019. Outline. Reinforcement Learning Multi-armed Bandit Problems Upper Confidence Bound (UCB) Thompson Sampling R code Implementation Comparison UCB and Thompson Sampling. What is reinforcement learning?. Data driven – Clustering &

560 views • 17 slides

Reinforcement Learning

Reinforcement Learning. Mitchell, Ch. 13 (see also Barto & Sutton book on-line). Rationale. Learning from experience Adaptive control Examples not explicitly labeled, delayed feedback Problem of credit assignment – which action(s) led to payoff?

405 views • 14 slides

Reinforcement Learning

Reinforcement Learning. Michael Roberts. With Material From: Reinforcement Learning: An Introduction Sutton & Barto (1998). What is RL?. Trial & error learning without model with model Structure. s3. r2. r1. s1. s2. r3. s4. RL vs. Supervised Learning.

556 views • 21 slides

Reinforcement Learning

Reinforcement Learning. Yijue Hou. What is learning? . Learning takes place as a result of interaction between an agent and the world, the idea behind learning is that

957 views • 34 slides

Reinforcement Learning

Reinforcement Learning. The study of thinking. 1) Problem-Solving 2) Reasoning. Perception Memory Thinking/Cognition Sensation Encoding Retrieval  ---------------------------------------------------------------------  Low Level Higher Level

493 views • 24 slides

Reinforcement Learning

Reinforcement Learning. Peter Bodík. Previous Lectures. Supervised learning classification, regression Unsupervised learning clustering, dimensionality reduction Reinforcement learning generalization of supervised learning learn from interaction w/ environment to achieve a goal.

927 views • 48 slides

Reinforcement Learning

Reinforcement Learning. Agenda. Online learning Reinforcement learning Model-free vs. model-based Passive vs. active learning Exploration-exploitation tradeoff. Incremental (“Online”) Function Learning. Data is streaming into learner x 1 ,y 1 , …, x n ,y n y i = f(x i )

1.06k views • 44 slides

Reinforcement Learning

Reinforcement Learning. Slides for this part are adapted from those of Dan Klein@UCB. Main Dimensions. Model-based vs. Model-free. Passive vs. Active. Passive vs. Active

917 views • 69 slides

Reinforcement Learning

Reinforcement Learning. 16 January 2009 RG Knowledge Based Systems Hans Kleine Büning. Outline. Motivation Applications Markov Decision Processes Q-learning Examples. How to program a robot to ride a bicycle?. Reinforcement Learning: The Idea.

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Reinforcement Learning

Reinforcement Learning. Vimal. The learner here is a decision making agent Keeps making decisions to take actions in the environment and receive rewards (or penalty ) A set of trial-and-error runs, the agent is expected to learn the best policy that maximize the total reward . .

686 views • 48 slides

Reinforcement Learning

Reinforcement Learning. Slides for this part are adapted from those of Dan Klein@UCB. Does self learning through simulator. [Infants don’t get to “simulate” the world since they neither have T(.) nor R(.) of their world]. Objective(s) of Reinforcement Learning. Given

871 views • 67 slides

Reinforcement Learning

Reinforcement Learning. Alan Fern . * Based in part on slides by Daniel Weld. So far …. Given an MDP model we know how to find optimal policies (for moderately-sized MDPs) Value Iteration or Policy Iteration Given just a simulator of an MDP we know how to select actions Monte-Carlo Planning

735 views • 52 slides

Reinforcement Learning

Reinforcement Learning. Slides from R.S. Sutton and A.G. Barto Reinforcement Learning: An Introduction http://www.cs.ualberta.ca/~sutton/book/the-book.html http://rlai.cs.ualberta.ca/RLAI/RLAIcourse/RLAIcourse.html. r. r. r. . . . . . . t +1. t +2. s. s. t +3. s. s. t +1.

965 views • 37 slides

Reinforcement Learning

Reinforcement Learning. Russell and Norvig: Chapter 21 CMSC 421 – Fall 2006. Reinforcement Learning. supervised learning is simplest and best-studied type of learning another type of learning tasks is learning behaviors when we don’t have a teacher to tell us how

360 views • 17 slides

REINFORCEMENT LEARNING

REINFORCEMENT LEARNING. Group 11 Ashish Meena 04005006 Rohitashwa Bhotica 04005010 Hansraj Choudhary 04d05005 Piyush Kedia 04d05009 . Outline. Introduction Learning Models Motivation Reinforcement Learning Framework Q – Learning Algorithm

1.02k views • 40 slides

Using Inaccurate Models in Reinforcement Learning

desired trajectory model-based controller our algorithm. S T A N F O R D. S T A N F O R D. 1. Preliminaries Markov Decision Process (MDP) M = ( S , A , T , H , s 0 , R ). S =  n (continuous state space) Time varying, deterministic dynamics

95 views • 1 slides

Reinforcement Learning

Reinforcement Learning. Chapter 21 Vassilis Athitsos. Reinforcement Learning. In previous chapters: Learning from examples. Reinforcement learning: Learning what to do. Learning to fly (a helicopter). Learning to play a game. Learning to walk. Learning based on rewards.

736 views • 26 slides

Hierarchical Reinforcement Learning Using Graphical Models

Hierarchical Reinforcement Learning Using Graphical Models. Victoria Manfredi and Sridhar Mahadevan Rich Representations for Reinforcement Learning ICML’05 Workshop August 7, 2005. Introduction. Abstraction necessary to scale RL hierarchical RL Want to learn abstractions automatically

397 views • 20 slides

Reinforcement Learning

Reinforcement Learning. HUT Spatial Intelligence course August/September 2004 Bram Bakker Computer Science, University of Amsterdam bram@science.uva.nl. Overview day 1 (Monday 13-16). Basic concepts Formalized model Value functions Learning value functions

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models: reinforcement learning &amp; fMRI

models: reinforcement learning &amp; fMRI

Presentation Transcript

talk-ppt - PowerPoint Presentation

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

REINFORCEMENT LEARNING

Using Inaccurate Models in Reinforcement Learning

Reinforcement Learning

Hierarchical Reinforcement Learning Using Graphical Models

Reinforcement Learning

models: reinforcement learning & fMRI

models: reinforcement learning & fMRI