Reinforcement learning AI techniques now available to the masses

Apr 02, 2019

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While neural networks are responsible for recent breakthroughs in problems like computer vision, machine translation and time series prediction u2013 they can also combine with reinforcement learning algorithms to create something astounding like AlphaGo. Learn how Reinforcement learning is different from some other types of machine learning.<br>

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Reinforcement learning AI techniques now available to the masses

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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?

372 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.

519 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

856 views • 34 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.

868 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 )

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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

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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 . .

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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

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Introduction to Reinforcement Learning

Introduction to Reinforcement Learning. Dr Kathryn Merrick k.merrick@adfa.edu.au 2008 Spring School on Optimisation, Learning and Complexity Friday 7 th November, 15:30-17:00. Reinforcement Learning is…. … learning from trial-and-error and reward by interaction with an environment.

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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.

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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

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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.

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Introduction to Reinforcement Learning

Introduction to Reinforcement Learning. Gerry Tesauro IBM T.J.Watson Research Center http://www.research.ibm.com/infoecon http://www.research.ibm.com/massdist. Outline. Statement of the problem: What RL is all about How it’s different from supervised learning Mathematical Foundations

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Applying Online Search Techniques to Reinforcement Learning

Applying Online Search Techniques to Reinforcement Learning. Scott Davies, Andrew Ng, and Andrew Moore Carnegie Mellon University. The Agony of Continuous State Spaces. Learning useful value functions for continuous-state optimal control problems can be difficult

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Reinforcement Learning

Reinforcement Learning. Mainly based on “Reinforcement Learning – An Introduction” by Richard Sutton and Andrew Barto Slides are mainly based on the course material provided by the same authors. http://www.cs.ualberta.ca/~sutton/book/the-book.html. Learning from Experience Plays a Role in ….

2.11k views • 136 slides

Reinforcement Learning

Reinforcement Learning. Presented by: Bibhas Chakraborty and Lacey Gunter. What is Machine Learning?. A method to learn about some phenomenon from data , when there is little scientific theory (e.g., physical or biological laws) relative to the size of the feature space.

1.21k views • 26 slides

Reinforcement Learning

470 views • 37 slides

Reinforcement Learning

Reinforcement Learning. Mainly based on “Reinforcement Learning – An Introduction” by Richard Sutton and Andrew Barto Slides are based on the course material provided by the same authors. http://www.cs.ualberta.ca/~sutton/book/the-book.html. Learning from Experience Plays a Role in ….

1.67k views • 134 slides

Reinforcement Learning

Reinforcement Learning. CPS 370 Ron Parr. Limitations of the MDP Framework. Assumes that transition probabilities are known How do we discover these? Envelope method assumed this problem away SDS had trouble collecting enough data How do we store them? Big problems have big models

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Reinforcement Learning

Reinforcement Learning. Problem: Find values for fixed policy  (policy evaluation) Model-based learning : Learn the model, solve for values Model-free learning : Solve for values directly (by sampling). The Story So Far: MDPs and RL. Techniques:. Things we know how to do:.

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Introduction to Reinforcement Learning

Introduction to Reinforcement Learning. Shijiang Lu. What Is Reinforcement Learning. Reinforcement learning (RL) is the problem facing an agent that must learn how to interact on a trial and error basis with a dynamic environment so that to maximize a scalar reward. Agent. s. r. a.

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