This document provides an introduction to reinforcement learning. It defines reinforcement learning and compares it to machine learning. Key concepts in reinforcement learning are discussed such as policy, reward function, value function and environment. Examples of reinforcement learning applications include chess, robotics, petroleum refineries. Model-free and model-based methods are introduced. The document also discusses Monte Carlo methods, temporal difference learning, and Dyna-Q architecture. Finally, it provides examples of reinforcement learning problems like elevator dispatching and job shop scheduling.
An efficient use of temporal difference technique in Computer Game LearningPrabhu Kumar
A computer game using temporal difference algorithm of Machine learning which improves the ability of the computer to learn and also explore the best next move for the game by greedy movement techniques and exploration method techniques for the future states of the game.
An efficient use of temporal difference technique in Computer Game LearningPrabhu Kumar
A computer game using temporal difference algorithm of Machine learning which improves the ability of the computer to learn and also explore the best next move for the game by greedy movement techniques and exploration method techniques for the future states of the game.
In real-world scenarios, decision making can be a very challenging task even for modern computers. Generalized reinforcement learning (GRL) was developed to facilitate complex decision making in highly dynamical systems through flexible policy generalization mechanisms using kernel-based methods. GRL combines the use of sampling, kernel functions, stochastic process, non-parametric regression and functional clustering.
In some applications, the output of the system is a sequence of actions. In such a case, a single action is not important
game playing where a single move by itself is not that important.in the case of the agent acts on its environment, it receives some evaluation of its action (reinforcement),
but is not told of which action is the correct one to achieve its goal
The lecture slides in DSAI 2018, National Cheng Kung University. It's about famous deep reinforcement learning algorithm: Actor-Critc. In this slides, we introduce advantage function, A3C/A2C.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Lecture slides in DASI spring 2018, National Cheng Kung University, Taiwan. The content is about deep reinforcement learning: policy gradient including variance reduction and importance sampling
This 10 hours class is intended to give students the basis to empirically solve statistical problems. Talk 1 serves as an introduction to the statistical software R, and presents how to calculate basic measures such as mean, variance, correlation and gini index. Talk 2 shows how the central limit theorem and the law of the large numbers work empirically. Talk 3 presents the point estimate, the confidence interval and the hypothesis test for the most important parameters. Talk 4 introduces to the linear regression model and Talk 5 to the bootstrap world. Talk 5 also presents an easy example of a markov chains.
All the talks are supported by script codes, in R language.
In real-world scenarios, decision making can be a very challenging task even for modern computers. Generalized reinforcement learning (GRL) was developed to facilitate complex decision making in highly dynamical systems through flexible policy generalization mechanisms using kernel-based methods. GRL combines the use of sampling, kernel functions, stochastic process, non-parametric regression and functional clustering.
In some applications, the output of the system is a sequence of actions. In such a case, a single action is not important
game playing where a single move by itself is not that important.in the case of the agent acts on its environment, it receives some evaluation of its action (reinforcement),
but is not told of which action is the correct one to achieve its goal
The lecture slides in DSAI 2018, National Cheng Kung University. It's about famous deep reinforcement learning algorithm: Actor-Critc. In this slides, we introduce advantage function, A3C/A2C.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Lecture slides in DASI spring 2018, National Cheng Kung University, Taiwan. The content is about deep reinforcement learning: policy gradient including variance reduction and importance sampling
This 10 hours class is intended to give students the basis to empirically solve statistical problems. Talk 1 serves as an introduction to the statistical software R, and presents how to calculate basic measures such as mean, variance, correlation and gini index. Talk 2 shows how the central limit theorem and the law of the large numbers work empirically. Talk 3 presents the point estimate, the confidence interval and the hypothesis test for the most important parameters. Talk 4 introduces to the linear regression model and Talk 5 to the bootstrap world. Talk 5 also presents an easy example of a markov chains.
All the talks are supported by script codes, in R language.
Chụp ảnh tình nguyện - Tập huấn nhóm truyền thông MHX 2013 - ĐHKHXH&NVGiang Pham
Chia sẻ kinh nghiệm cá nhân về việc chụp ảnh khi tham gia các chiến dịch tình nguyện, đặc biệt là Mùa hè xanh.
Tài liệu tập huấn cho MHX 2013 trường ĐHKHXH&NV.
Xây dựng cá nhân và văn hóa tổ chức (by Red Bear)Giang Pham
Các vấn đề về cá nhân và xây dựng văn hóa tổ chức trong hoạt động sinh viên.
Tài liệu tập huấn Đoàn - Hội - CLB khoa QHQT 2013.
Thực hiện bởi Tạ Thái Minh Phúc.
Matineh Shaker, Artificial Intelligence Scientist, Bonsai at MLconf SF 2017MLconf
Deep Reinforcement Learning with Shallow Trees:
In this talk, I present Concept Network Reinforcement Learning (CNRL), developed at Bonsai. It is an industrially applicable approach to solving complex tasks using reinforcement learning, which facilitates problem decomposition, allows component reuse, and simplifies reward functions. Inspired by Sutton’s options framework, we introduce the notion of “Concept Networks” which are tree-like structures in which leaves are “sub-concepts” (sub-tasks), representing policies on a subset of state space. The parent (non-leaf) nodes are “Selectors”, containing policies on which sub-concept to choose from the child nodes, at each time during an episode. There will be a high-level overview on the reinforcement learning fundamentals at the beginning of the talk.
Bio: Matineh Shaker is an Artificial Intelligence Scientist at Bonsai in Berkeley, CA, where she builds machine learning, reinforcement learning, and deep learning tools and algorithms for general purpose intelligent systems. She was previously a Machine Learning Researcher at Geometric Intelligence, Data Science Fellow at Insight Data Science, Predoctoral Fellow at Harvard Medical School. She received her PhD from Northeastern University with a dissertation in geometry-inspired manifold learning.
A review of literature shows that there is a variety of works studying coverage path planning in several autonomous robotic applications. In this work, we propose a new approach using Markov Decision Process to plan an optimum path to reach the general goal of exploring an unknown environment containing buried mines. This approach, called Goals to Goals Area Coverage on-line Algorithm, is based on a decomposition of the state space into smaller regions whose states are considered as goals with the same reward value, the reward value is decremented from one region to another according to the desired search mode. The numerical simulations show that our approach is promising for minimizing the necessary cost-energy to cover the entire area.
this talk was an introduction to Reinforcement Learning based on the book by Andrew Barto and Richard S. Sutton. We explained the main components of an RL problem and detailed the tabular solutions and approximate solutions methods.
TensorFlow and Deep Learning Tips and TricksBen Ball
Presented at https://www.meetup.com/TensorFlow-and-Deep-Learning-Singapore/events/241183195/ . Tips and Tricks for using Tensorflow with Deep Reinforcement Learning.
See our blog for more information at http://prediction-machines.com/blog/
Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
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Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
7. REINFORCEMENT LEARNING
WORKS ON PRIOR AND
ACCURATE MEASUREMENTS
IT WORKS ON SOME
CALCULATIONS AND PLACES
WHERE HUMAN CAN'T REACH. IT
IS INTANGIBLE
8. IT IS NOT A REAL WORLD
PROBLEM BUT TRYING TO
REACH THINGS BEYOND THE
WORLD!
9. Reinforcement learning is
learning what to do--how to map situations to actions--so as to
maximize a numerical reward signal.
These two characteristics--trial-and-error search and delayed
reward--are the two most important distinguishing features of
reinforcement learning.
10. EXAMPLES
➢ CHESS BOARD MOVEMENTS
➢ ROBOTICS MOVEMENTS
➢ DEPTH OF THE PETROLEUM REFINERY
AND ADAPTOR
11. Comparison Between Machine Learning
And Reinforcement Learning.
Machine learning works classified and unclassified data sets.
Reinforcement learning requires prior – accurate measurements.
Example, Robotics Arms should be measured and given, how many
steps to move and in what direction based on temperature adaptors.
Machine learning is a real-world problem. Reinforcement learning
works on some calculations, and helps in places where man can not
reach. Example, Petroleum Refineries.
Machine learning is tangible. Reinforcement learning is intangible.
Example, Chandrayan-1 project.
Machine learning works on sequence of step, reinforcement learning
works on episodesepisodes
14. POLICY, REWARD AND VALUE
➢ POLICY DEFINES LEARNING AGENTS'S
WAY OF BEHAVING AT A GIVEN TIME.
➢ REWARD DEFINES A GOAL IN A
REINFORECEMENT PROBLEM
➢ VALUE FUNCTION SPECIFIES WHAT IS
GOOD IN THE LONG RUN
15. ENVIRONMENT
The fourth and final element of some reinforcement learning systems is a
model of the environment. This is something that mimics the behavior of the
environment. For example, given a state and action, the model might predict
the resultant next state and next reward. Models are used for planning
16.
17. An -Armed Bandit Problem
An analogy is that of a doctor choosing
between experimental treatments for a
series of seriously ill patients. Each play
is a treatment selection, and each
reward is the survival or well-being of
the patient.
18. In our -armed bandit problem,
Each action has an expected or mean reward given
that the action is selected; let us call this the value of
that action. If you knew the value of each action,
then it would be trivial to solve the -armed bandit
problem: you would always select the action with
highest value. We assume that you do not know the
action values with certainty, although you may have
estimates.
19.
20. Monte Carlo Methods
Monte Carlo methods are ways of solving the reinforcement learning problem based on averaging
sample returns. To ensure that well-defined returns are available, we define Monte Carlo methods only
for episodic tasks. That is, we assume experience is divided into episodes, and that all episodes
eventually terminate no matter what actions are selected. It is only upon the completion of an episode
that value estimates and policies are changed. Monte Carlo methods are thus incremental in an
episode-by-episode sense, but not in a step-by-step sense. The term "Monte Carlo" is often used more
broadly for any estimation method whose operation involves a significant random component. Here we
use it specifically for methods based on averaging complete returns.
23. The spectrum ranging from the one-step backups
of simple TD methods(step-by-step value) to the
up-until-termination backups of Monte Carlo
methods(next episode reward)
24. Performance of -step TD methods. The performance measure
shown is the root mean-squared (RMS) error between the true
values of states and the values found by the learning methods,
averaged over the 19 states, the first 10 trials, and 100 different
sequences of walks.
25. A simple maze (inset) and the average learning curves
for Dyna-Q agents varying in their number of planning
steps per real step. The task is to travel from S to S as
quickly as possible.
26. Action-Value Methods
we denote the true (actual) value of action a as, Q*(a) and the
estimated value at the rth play as Qt(a) . Recall that the true value
of an action is the mean reward received when that action is
selected. One natural way to estimate this is by averaging the
rewards actually received when the action was selected. And r be
the number of plays in which Ka is the times prior to rth play.
30. DYNAMIC PROGRAMMING
The reinforcement learning is more useful in
tracking the Non-stationary problems.
In such cases it makes sense to weight recent
rewards more heavily than long-past ones. Ex.
DYNAMIC PROGRAMMING.
31. The Agent-Environment Interface
The reinforcement learning problem is meant to
be a straightforward framing of the problem of
learning from interaction to achieve a goal. The
learner and decision-maker is called the agent.
The thing it interacts with, comprising
everything outside the agent, is called the
environment.
32. The Markov Property
In the reinforcement learning framework, the
agent makes its decisions as a function of a signal
from the environment called the environment's
state. In particular, we formally define a property of
environments and their state signals that is of
particular interest, called the Markov property.
33. Example 1: Bioreactor Suppose reinforcement
learning is being applied to determine
moment-by-moment temperatures and stirring
rates for a bioreactor (a large vat of nutrients and
bacteria used to produce useful chemicals)
34. Example 2: Pick-and-Place Robot Consider using
reinforcement learning to control the motion of a
robot arm in a repetitive pick-and-place task. If we
want to learn movements that are fast and
smooth, the learning agent will have to control the
motors directly and have low-latency information
about the current positions and velocities of the
mechanical linkages.
36. (DEFUN full-speed-ahead (r s dir)
“This will make the rcx in stream R go at speed S
in direction DIR until touch sensor on its ‘2’ port returns 1.”
(LET ((result 0))
(set-effector-state ‘(:A :B :C) :power :off r)
;in case things are in an inconsistent state,
;turn everything off first
(set-effector-state ‘(:A :C) :speed s r)
(set-effector-state ‘(:A :C) :direction dir r)
; dir is eq to :forward, :backward, or :toggle
; no motion will occur until the
; next call to set-effector-state
(set-sensor-state 2 :type :touch :mode :boolean r)
(set-effector-state ‘(:A :C) :power :on r)
(LOOP ;this loop will repeat forever until sensor 2 returns a 1
(SETF result (sensor 2 r))
(WHEN (AND (NUMBERP result)
;needed to keep = from causing error if
;sensor function returns nil due to timeout.
(= result 1))
(RETURN)))
(set-effector-state ‘(:A :C) :power :float r))))
(DEFUN testing ()
(with-open-com-port (port :LEGO-USB-TOWER)
(with-open-rcx-stream (rcx10 port :timeout-interval 80 :rcx-unit 10)
; increase/decrease serial timeout value of 80 ms depending on
;environmental factors like ambient light.
(WHEN (alivep rcx10)
(full-speed-ahead rcx10 5 :forward)))))
37. Integrating Planning, Acting, and
Learning
When planning is done on-line, while
interacting with the environment, a number
of interesting issues arise. New information
gained from the interaction may change the
model and thereby interact with planning. It
may be desirable to customize the planning
process in some way to the states or decisions
currently under consideration, or expected in
the near future.
39. Dyna-Q, a simple architecture integrating the major functions
needed in an on-line planning agent. Each function appears in
Dyna-Q in a simple, almost trivial, form
41. A simple maze (inset) and the average learning
curves for Dyna-Q agents varying in their number of
planning steps per real step. The task is to travel
from S to S as quickly as possible.
42. Policies found by planning and nonplanning Dyna-Q
agents halfway through the second episode. The arrows
indicate the greedy action in each state; no arrow is
shown for a state if all of its action values are equal. The
black square indicates the location of the agent.
43. Dimensions of Reinforcement Learning
The dimensions of the reinforcement learning have to do with
the kind of backup used to improve the value function. The
vertical dimension is whether they are sample backups (based
on a sample trajectory) or full backups (based on a distribution
of possible trajectories). Full backups of course require a
model, whereas sample backups can be done either with or
without a model (another dimension of variation).
44. A slice of the space of
reinforcement learning methods.
45. The horizontal dimension corresponds to the depth of backups,
that is, to the degree of bootstrapping. At three of the four
corners of the space are the three primary methods for
estimating values: DP, TD, and Monte Carlo.
A third important dimension is that of function approximation.
Function approximation can be viewed as an orthogonal
spectrum of possibilities ranging from tabular methods at one
extreme through state aggregation, a variety of linear methods,
and then a diverse set of nonlinear methods
46. In addition to the four dimensions just discussed, we have
identified a number of others throughout the book:
Definition of return
Is the task episodic or continuing, discounted or undiscounted?
Action values vs. state values vs. afterstate values
What kind of values should be estimated? If only state values
are estimated, then either a model or a separate policy (as in
actor-critic methods) is required for action selection.
Action selection/exploration
How are actions selected to ensure a suitable trade-off
between exploration and exploitation? We have considered
only the simplest ways to do this: -greedy and softmax action
selection, and optimistic initialization of values.
47. Synchronous vs. asynchronous
Are the backups for all states performed
simultaneously or one by one in some order?
Replacing vs. accumulating traces
If eligibility traces are used, which kind is most
appropriate?
Real vs. simulated
Should one backup real experience or simulated
experience? If both, how much of each?
48. Location of backups
What states or state-action pairs should be backed up?
Modelfree methods can choose only among the states
and state-action pairs actually encountered, but
model-based methods can choose arbitrarily. There are
many potent possibilities here.
Timing of backups
Should backups be done as part of selecting actions, or
only afterward?
Memory for backups
How long should backed-up values be retained? Should
they be retained permanently, or only while computing an
action selection, as in heuristic search?
49. Elevator Dispatching
Waiting for an elevator is a situation :
We press a button and then wait for an elevator to arrive traveling in the right
direction.
We may have to wait a long time if there are too many passengers or not enough
elevators.
Just how long we wait depends on the dispatching strategy the elevators use to
decide where to go.
For example, if passengers on several floors have requested pickups, which
should be served first? If there are no pickup requests, how should the elevators
distribute themselves to await the next request?
Elevator dispatching is a good example of a stochastic optimal control problem of
economic importance that is too large to solve by classical techniques such as
dynamic programming.
51. Job-Shop Scheduling
Many jobs in industry and elsewhere require completing a collection of tasks
while satisfying temporal and resource constraints. Temporal constraints say that
some tasks have to be finished before others can be started; resource constraints
say that two tasks requiring the same resource cannot be done simultaneously
(e.g., the same machine cannot do two tasks at once). The objective is to create a
schedule specifying when each task is to begin and what resources it will use that
satisfies all the constraints while taking as little overall time as possible. This is
the job-shop scheduling problem.
52. JOB SHOP SCHDULING APPLICATION IN
NASA
The NASA space shuttle payload processing
problem (SSPP), which requires scheduling the
tasks required for installation and testing of
shuttle cargo bay payloads. An SSPP typically
requires scheduling for two to six shuttle
missions, each requiring between 34 and 164
tasks. An example of a task is
MISSION-SEQUENCE-TEST.
53. CONCLUSION
All of the reinforcement learning methods we have explored in this
ppt have three key ideas in common.
First, the objective of all of them is the estimation of value functions.
Second, all operate by backing up values along actual or possible
state trajectories.
Third, all follow the general strategy of generalized policy iteration
(GPI), meaning that they maintain an approximate value function
and an approximate policy, and they continually try to improve each
on the basis of the other.
We suggest that value functions, backups, and GPI are powerful
organizing principles potentially relevant to any model of
intelligence.