This document summarizes an efficient use of temporal difference techniques in computer game learning. It discusses reinforcement learning and some key concepts including the agent-environment interface, types of reinforcement learning tasks, elements of reinforcement learning like policy, reward functions, and value functions. It also describes algorithms like dynamic programming, policy iteration, value iteration, and temporal difference learning. Finally, it mentions some applications of reinforcement learning in benchmark problems, games, and real-world domains like robotics and control.
Slides from my presentation of Richard Sutton and Andrew Barto's "Introduction to Reinforcement Learning Chapter 1"
Video (https://www.youtube.com/watch?v=4SLGEq_HZxk&t=2s)
Reinforcement Learning Guide For Beginnersgokulprasath06
Reinforcement Learning Guide:
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The Reinforcement Learning (RL) is a particular type of learning. It is useful when we try to learn from an unknown environment. Which means, that our model will have to explore the environment in order to collect the necessary data to use for its training. The model is represented as an Agent, trying to achieve a certain goal in a particular environment. The Agent affects this environment by taking actions that change the state of the environment and generate rewards produced by this later one.
The learning relies on the generated rewards, and the goal will be to maximize them. To choose the actions to apply, the agents use a policy. It can be defined as the process that the agent use to choose the actions that will permit it to optimize the overall rewards. In this course, we will see two methods used to develop these polices: policy gradient and Q-Learning. We will implement our examples using the following libraries: OpenAI gym, keras , tensorflow and keras-rl.
[Notebook 1](https://colab.research.google.com/drive/1395LU6jWULFogfErI8CIYpi35Y00YiRj)
[Notebook 2](https://colab.research.google.com/drive/1MpDS5rj-PwzzLIZtAGYnZ_jjEwhWZEdC)
Slides from my presentation of Richard Sutton and Andrew Barto's "Introduction to Reinforcement Learning Chapter 1"
Video (https://www.youtube.com/watch?v=4SLGEq_HZxk&t=2s)
Reinforcement Learning Guide For Beginnersgokulprasath06
Reinforcement Learning Guide:
Land in multiple job interviews by joining our Data Science certification course.
Data Science course content designed uniquely, which helps you start learning Data Science from basics to advanced data science concepts.
Content: http://bit.ly/2Mub6xP
Any Queries, Call us@ +91 9884412301 / 9600112302
The Reinforcement Learning (RL) is a particular type of learning. It is useful when we try to learn from an unknown environment. Which means, that our model will have to explore the environment in order to collect the necessary data to use for its training. The model is represented as an Agent, trying to achieve a certain goal in a particular environment. The Agent affects this environment by taking actions that change the state of the environment and generate rewards produced by this later one.
The learning relies on the generated rewards, and the goal will be to maximize them. To choose the actions to apply, the agents use a policy. It can be defined as the process that the agent use to choose the actions that will permit it to optimize the overall rewards. In this course, we will see two methods used to develop these polices: policy gradient and Q-Learning. We will implement our examples using the following libraries: OpenAI gym, keras , tensorflow and keras-rl.
[Notebook 1](https://colab.research.google.com/drive/1395LU6jWULFogfErI8CIYpi35Y00YiRj)
[Notebook 2](https://colab.research.google.com/drive/1MpDS5rj-PwzzLIZtAGYnZ_jjEwhWZEdC)
Reinforcement Learning 8: Planning and Learning with Tabular MethodsSeung Jae Lee
A summary of Chapter 8: Planning and Learning with Tabular Methods of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.ai
These slides presents the optimization using evolutionary computing techniques. Particle Swarm Optimization and Genetic Algorithm are discussed in detail. Apart from that multi-objective optimization are also discussed in detail.
SEARN Algorithm is a search-based algorithm for structured prediction.
Most of the content is taken from http://users.umiacs.umd.edu/~hal/docs/daume06thesis.pdf. I just read the thesis and presented what's in there. Thus the credits of the content should go to the author of the thesis.
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Reinforcement Learning 5. Monte Carlo MethodsSeung Jae Lee
A summary of Chapter 5: Monte Carlo Methods of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.ai
Financial Trading as a Game: A Deep Reinforcement Learning Approach謙益 黃
An automatic program that generates constant profit from the financial market is lucrative for every market practitioner. Recent advance in deep reinforcement learning provides a framework toward end-to-end training of such trading agent. In this paper, we propose an Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm. We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1. We employ a substantially small replay memory (only a few hundreds in size) compared to ones used in modern deep reinforcement learning algorithms (often millions in size.) 2. We develop an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent. This enables us to use greedy policy over the course of learning and shows strong empirical performance compared to more commonly used ε-greedy exploration. However, this technique is specific to financial trading under a few market assumptions. 3. We sample a longer sequence for recurrent neural network training. A side product of this mechanism is that we can now train the agent for every T steps. This greatly reduces training time since the overall computation is down by a factor of T. We combine all of the above into a complete online learning algorithm and validate our approach on the spot foreign exchange market.
Multiobjective optimization and Genetic algorithms in ScilabScilab
In this Scilab tutorial we discuss about the importance of multiobjective optimization and we give an overview of all possible Pareto frontiers. Moreover we show how to use the NSGA-II algorithm available in Scilab.
Linear Programming Problems {Operation Research}FellowBuddy.com
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
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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.
Reinforcement Learning 8: Planning and Learning with Tabular MethodsSeung Jae Lee
A summary of Chapter 8: Planning and Learning with Tabular Methods of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.ai
These slides presents the optimization using evolutionary computing techniques. Particle Swarm Optimization and Genetic Algorithm are discussed in detail. Apart from that multi-objective optimization are also discussed in detail.
SEARN Algorithm is a search-based algorithm for structured prediction.
Most of the content is taken from http://users.umiacs.umd.edu/~hal/docs/daume06thesis.pdf. I just read the thesis and presented what's in there. Thus the credits of the content should go to the author of the thesis.
Adversarially Guided Actor-Critic, Y. Flet-Berliac et al, 2021Chris Ohk
RL 논문 리뷰 스터디에서 Adversarially Guided Actor-Critic 논문 내용을 정리해 발표했습니다. AGAC는 Actor-Critic에 GAN에서 영감을 받은 방법들을 결합해 리워드가 희소하고 탐험이 어려운 환경에서 뛰어난 성능을 보여줍니다. 많은 분들에게 도움이 되었으면 합니다.
Reinforcement Learning 5. Monte Carlo MethodsSeung Jae Lee
A summary of Chapter 5: Monte Carlo Methods of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.ai
Financial Trading as a Game: A Deep Reinforcement Learning Approach謙益 黃
An automatic program that generates constant profit from the financial market is lucrative for every market practitioner. Recent advance in deep reinforcement learning provides a framework toward end-to-end training of such trading agent. In this paper, we propose an Markov Decision Process (MDP) model suitable for the financial trading task and solve it with the state-of-the-art deep recurrent Q-network (DRQN) algorithm. We propose several modifications to the existing learning algorithm to make it more suitable under the financial trading setting, namely 1. We employ a substantially small replay memory (only a few hundreds in size) compared to ones used in modern deep reinforcement learning algorithms (often millions in size.) 2. We develop an action augmentation technique to mitigate the need for random exploration by providing extra feedback signals for all actions to the agent. This enables us to use greedy policy over the course of learning and shows strong empirical performance compared to more commonly used ε-greedy exploration. However, this technique is specific to financial trading under a few market assumptions. 3. We sample a longer sequence for recurrent neural network training. A side product of this mechanism is that we can now train the agent for every T steps. This greatly reduces training time since the overall computation is down by a factor of T. We combine all of the above into a complete online learning algorithm and validate our approach on the spot foreign exchange market.
Multiobjective optimization and Genetic algorithms in ScilabScilab
In this Scilab tutorial we discuss about the importance of multiobjective optimization and we give an overview of all possible Pareto frontiers. Moreover we show how to use the NSGA-II algorithm available in Scilab.
Linear Programming Problems {Operation Research}FellowBuddy.com
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
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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.
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발표영상:
http://tv.naver.com/v/2024376
https://youtu.be/dw0sHzE1oAc
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An efficient use of temporal difference technique in Computer Game Learning
1. An Efficient use of temporal difference technique in computer game
learning
Indian institute of technology
( Indian school of mines ),
Dhanbad.
Project guide:- Presented by:
Dr. Rajendra Pamula Prabhu Kumar
Department of computer 15MT000624
Science and engineering Computer science and engineering
Indian institute of technology Indian institute of technology
( Indian school of mines), ( Indian school of mines),
Dhanbad Dhanbad
2. Outline
1. Introduction of reinforcement learning
2. Agent-Environment interface
3. Types of reinforcement learning
4. Elements of the reinforcement learning
5. Types of selection of state
6. Algorithms of reinforcement learning
References
3. Introduction of reinforcement learning
Reinforcement learning is the part of machine learning, which is a field of computer
science that gives computer to ability to learn without being explicitly programmed.
Reinforcement learning is a framework for computational learning agents use experience
from their interaction with an environment to improve performance over time.
In reinforcement learning task, the agent understands the state of the environment and it
always tries to maximize the long-term return which is based on real value reward.
It is learning of what to do-how to do mapping situation to action so as maximize total
numerical reward and minimize the penalty.
4. Introduction of reinforcement learning cont.…
• If there is no explicit teacher to guide the learning agent, the agent must learn the behavior
through trail-and-error interaction with unknown environment.
• The learning agent senses the environment, takes actions on it, and receives numeric reward or
punishment from some reward function.
• When we say agent learn means ”sometimes it modifies the code itself or modifies the database ”,
database implies the experiences, information, event etc.
• It is responsible for making decision.
• The main goal of reinforcement learning is “Buildup a good model such as algorithm which
generate a sequence of decision and lead to the highest long-term reward.”
5. Agent environment interface
o At each time step t, the reinforcement learning agent
receives some representation of environment’s current state
s(t) € S ,where S is the set of possible state and then choose
some action a(t)€ A(st), where A(st) is set of actions that can
be executed in state s(t).
o The agent receives reward r(t+1) and execute in next state
s(t+1)
o The reward function can be used for specify the wide range
of planning goals, It means the designer can tell the agent
what he has achieve.
o The reward function which must be unalterable by the
agent.
6. Types of reinforcement learning
There are two types of reinforcement learning
1. Episodic: The interaction with the environment is divided into independent episodes.
“Independent”, means performance in each episode is depends only the action taken on that
episode.
in episodic task, a return is sum of all reward received from the beginning of the episode until ends.
where, T is terminal state i.e. end of episode ends
S0 is the starting state of
R denotes as total return
r(k) denotes as the reward on the kth states
7. Types of the reinforcement learning contd..
2.Continuing task: It consist infinite sequence of state, action and rewards. In this task, the action
and environment interaction doesn’t break down in separate episode. The performance is depends
upon the current action.
In the case of continuing task, The return is depends upon discount factor
where γ denotes discount factor which adjust the relative importance
between long-term consequences vs. short term consequences.
The discount factor is between 0 and 1.
The discount factor reflects the strategy of how fast learning takes place
If γ =0, agent only concerned about maximizing the immediate rewards
If γ approaches to 1, The agent takes the future reward into account
8. Element of the reinforcement learning
1. Policy:
It defines the learning agent’s way of behaving at a given time.
It might be a function or simple lookup table.
It only used in reinforcement learning is to determine the behavior.
2. Reward function:
It is the function which defines which one is the bad and good event for agent.
It maps each state-action pair of the environment to a single real number.
It must necessarily unalterable by the agent.
9. Elements in the reinforcement learning contd..
3. Value function:
It specifies what is good in long run. The value of the state is the total amount of reward an
agent can expect to accumulate over the future, starting from that state.
Where as reward is the immediate desirability of environmental states. i.e. values indicate the
long-tem desirability of states.
4. Model:
It is used for planning. It defines the copy of behavior, e.g. by given state and action ,The model
might predict the next state and next reward.
10. Algorithm of reinforcement learning
1. Markov decision processes:
• It is standard, general formalism for sequential decision problems.
• It consist tuple of <S,A,P,R>
where S is the set of states.
A is the set of actions available to the agent
P is the probability, P(a, ss′) = P r {st+1 = s ′ | st = s, at = a}, it is a state transition function that
defines the probability of transitioning to state s ′ at time t + 1 after action a is taken when agent is
in state s at time t.
R is the reward function that determines the probability of receiving reward after choosing action a
in state s and going for next state s’.
11. Algorithms in reinforcement learning
2. Dynamic programming (DP)
• It is the method to solve the markov decision process i.e. to find an optimal policy, if the full
knowledge of model is available.
• For dynamic programming, all the transition probabilities and reward expectation must be known.
• This algorithm updates the estimates of states values based on their estimates of the next state.
• There are two basic DP methods used for computing optimal policy
1. Policy iteration
2. Value iteration
12. Policy Iteration:
• It forms a sequence of policy Ωo, Ω1, Ω2….Ωk, Ωk+1 where Ωk+1 is an improvement of Ωk.
• Policy evaluation task is concerned with computing state value function for any policy Ω
• The iterative algorithm for policy evaluation is
• Estimating value functions is particularly useful for finding the better policy.
• The policy improvement algorithm uses action-value function to improve the current policy. If
then it is better to select
action a in policy Ω
• If Ω and Ω’ are two policy and this condition hold then Ω’ is the better policy than Ω
13. Value iteration
• In value iteration, optimal policy is not computed directly.
• For that, optimal value function is computed and then a greedy policy with respect to function is
an optimal policy.
• It stops and find the optimal policy when the changes introduced by backups/updates becomes
sufficiently small.
• One threshold value has been initialized and compared with threshold value.
• If the policy value is sufficiently smaller than threshold, the policy is called as optimal policy
14. 3. Temporal difference
• The temporal differences idea has been taken from dynamic programming.
• The temporal difference and dynamic programming, both are used for accumulating the value functions.
• In this methods, learning takes place after every time step which is beneficial as it makes for efficient
learning
• The agent can revise its policy after every action and state it experiences.
• TD algorithms make updates of the estimated policy values based on each state transition and on the
immediate reward received from the environment on this transition.
• The initial temporal difference algorithm is called TD(0),called tabular estimates v(Ω).
It updates by following method
V (s) ← V (s) + α (V (s’) − V (s)) where α is the positive step size parameter
V(s’) is the value function for next state
α (V (s’) − V (s)), called as Temporal difference error. V(s) is the value function for the current state
Which is always designed to move toward 0.
15. Type of selection of states
1. Greedy
2. Exploration process
a) Providing initial knowledge
b) Deriving a policy from demonstration
c) Ask for help
d) Teacher provide advice
16. Application of reinforcement learning
1. Benchmark Problems
a) Mountain car
b) Cart-pole balancing
c) Pendulum swing up tec..
2. Games
a) Tic-Tac-Toe
b) Chess etc..
3. Real world applications
a) Robotics
b) Control of helicopter
c) Prediction of stock prices
17. References
• R. S. Sutton. Reinforcement learning: past, present and future [online]. Available from http:
//www-anw. cs. umass. edu/ rich/Talks/SEAL98/SEAL98. html [accessed on December 2005]. 1999.
• R. S. Sutton and A. G. Barto. Reinforcement learning. an introduction. Cambridge, MA: The MIT
Press, 1998.
• M. L. Puterman. Markov decision processes-discrete stochastic dynamic programming. John
Wiley and sons, Inc, New York, NY, 1994.