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Reinforcement learning and control

We now begin our study of reinforcement learning and adaptive control.

In supervised learning, we saw algorithms that tried to make their outputs mimic the labels y given in the training set. In that setting, the labels gave an unambiguous“right answer” for each of the inputs x . In contrast, for many sequential decision making and control problems, it is very difficult to provide thistype of explicit supervision to a learning algorithm. For example, if wehave just built a four-legged robot and are trying to program it to walk, then initially we have no idea what the “correct” actions to take are to make itwalk, and so do not know how to provide explicit supervision for a learning algorithm to try to mimic.

In the reinforcement learning framework, we will instead provide our algorithms only a reward function, which indicates to the learning agent when itis doing well, and when it is doing poorly. In the four-legged walking example, the reward function might give therobot positive rewards for moving forwards, and negative rewards for either moving backwards or falling over. It will then be the learningalgorithm's job to figure out how to choose actions over time so as to obtain large rewards.

Reinforcement learning has been successful in applications as diverse as autonomous helicopter flight, robot legged locomotion, cell-phone networkrouting, marketing strategy selection, factory control, and efficient web-page indexing.Our study of reinforcement learning will begin with a definition of the Markov decision processes (MDP) , which provides the formalism in which RL problems are usually posed.

Markov decision processes

A Markov decision process is a tuple ( S , A , { P s a } , γ , R ) , where:

  • S is a set of states . (For example, in autonomous helicopter flight, S might be the set of all possible positions and orientations of the helicopter.)
  • A is a set of actions . (For example, the set of all possible directions in which you can push the helicopter's control sticks.)
  • P s a are the state transition probabilities. For each state s S and action a A , P s a is a distribution over the state space. We'll say more about this later, but briefly, P s a gives the distribution over what states we will transition to if we takeaction a in state s .
  • γ [ 0 , 1 ) is called the discount factor .
  • R : S × A R is the reward function . (Rewards are sometimes also written as a function of a state S only, in which case we would have R : S R ).

The dynamics of an MDP proceeds as follows: We start in some state s 0 , and get to choose some action a 0 A to take in the MDP. As a result of our choice, the state of the MDPrandomly transitions to some successor state s 1 , drawn according to s 1 P s 0 a 0 . Then, we get to pick another action a 1 . As a result of this action, the state transitions again, now tosome s 2 P s 1 a 1 . We then pick a 2 , and so on.... Pictorially, we can represent this process as follows:

s 0 a 0 s 1 a 1 s 2 a 2 s 3 a 3 ...

Upon visiting the sequence of states s 0 , s 1 , ... with actions a 0 , a 1 , ... , our total payoff is given by

R ( s 0 , a 0 ) + γ R ( s 1 , a 1 ) + γ 2 R ( s 2 , a 2 ) + .

Questions & Answers

find the 15th term of the geometric sequince whose first is 18 and last term of 387
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I know this work
The given of f(x=x-2. then what is the value of this f(3) 5f(x+1)
virgelyn Reply
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kinnecy Reply
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I'm not sure why it wrote it the other way
I got X =-6
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oops. ignore that.
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Commplementary angles
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a perfect square v²+2v+_
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algebra 2 Inequalities:If equation 2 = 0 it is an open set?
Kim Reply
or infinite solutions?
The answer is neither. The function, 2 = 0 cannot exist. Hence, the function is undefined.
Embra Reply
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rolling four fair dice and getting an even number an all four dice
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Kristine 2*2*2=8
Bridget Reply
Differences Between Laspeyres and Paasche Indices
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No. 7x -4y is simplified from 4x + (3y + 3x) -7y
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. After 3 months on a diet, Lisa had lost 12% of her original weight. She lost 21 pounds. What was Lisa's original weight?
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I start with an easy one. carbon nanotubes woven into a long filament like a string
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Yes, Nanotechnology has a very fast field of applications and their is always something new to do with it...
Himanshu Reply
good afternoon madam
what is system testing
what is the application of nanotechnology?
In this morden time nanotechnology used in many field . 1-Electronics-manufacturad IC ,RAM,MRAM,solar panel etc 2-Helth and Medical-Nanomedicine,Drug Dilivery for cancer treatment etc 3- Atomobile -MEMS, Coating on car etc. and may other field for details you can check at Google
anybody can imagine what will be happen after 100 years from now in nano tech world
after 100 year this will be not nanotechnology maybe this technology name will be change . maybe aftet 100 year . we work on electron lable practically about its properties and behaviour by the different instruments
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how hard could it be to apply nanotechnology against viral infections such HIV or Ebola?
silver nanoparticles could handle the job?
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I'm interested in Nanotube
this technology will not going on for the long time , so I'm thinking about femtotechnology 10^-15
can nanotechnology change the direction of the face of the world
Prasenjit Reply
At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
Ali Reply
the Beer law works very well for dilute solutions but fails for very high concentrations. why?
bamidele Reply
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Source:  OpenStax, Machine learning. OpenStax CNX. Oct 14, 2013 Download for free at http://cnx.org/content/col11500/1.4
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