aprendizaje por refuerzo
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El documento describe el aprendizaje por refuerzo, que consiste en que un agente aprende mediante prueba y error cuáles acciones tomar en diferentes estados para lograr una meta. Explica los elementos básicos de este modelo de aprendizaje y métodos como el perceptrón para implementarlo. Finalmente, concluye que las redes neuronales son fundamentales para la inteligencia artificial y pueden aplicarse a muchos problemas.
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![INTELIGENCIA ARTIFICIAL ,[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recomendados
Types of environment
Types of environment, Fully observable vs partially observable, static vs dynamic, deterministic vs stochastic, episodic vs sequential, discrete vs continuous, single agent vs multiagent, single agent vs multiagent,
Reinforcement Learning
1) Reinforcement Learning 개요
- Markov Decision Processes
- 기본적인 강화학습의 개념
2) Planning vs Reinforcement Learning
- Reinforcement Learning과 Planning의 차이
- Model Dynamics 학습
- TD(Temporal Difference) 기반의 Reinforcement Learning
3) Reinforcement Learning 실습
- 간단한 2차원 게임에서의 실습 리뷰
http://www.fastcampus.co.kr/data_workshop_dla/
Intro to Reinforcement learning - part III
Introduction to Reinforcement Learning, part III: Basic approximate methods
This is the final presentation in a three-part series covering the basics of Reinforcement Learning (RL).
In this presentation, we introduce value function approximation and cover three different approaches to generating features for linear models.
We then take a sidestep to cover stochastic gradient descent in some detail before we return to introduce semi-gradient descent for RL. We also briefly cover a batch method as an alternative for episodic methods.
We discuss the implementation of the RL algorithms. For further discussion and illustrating the simulation results, we refer to Github repositories with source code of the implementation as well as Jupyter notebooks visualizing the simulation results.
Deep Reinforcement Learning
Deep Reinforcement Learning Talk at PI School. Covering following contents as:
1- Deep Reinforcement Learning
2- QLearning
3- Deep QLearning (DQN)
4- Google Deepmind Paper (DQN for ATARI)
Reinforcement Learning : A Beginners Tutorial
This a presentation of a Reinforcement Learning tutorial for beginners which I worked on.
Recomendados
Types of environment
Types of environment, Fully observable vs partially observable, static vs dynamic, deterministic vs stochastic, episodic vs sequential, discrete vs continuous, single agent vs multiagent, single agent vs multiagent,
Reinforcement Learning
1) Reinforcement Learning 개요
- Markov Decision Processes
- 기본적인 강화학습의 개념
2) Planning vs Reinforcement Learning
- Reinforcement Learning과 Planning의 차이
- Model Dynamics 학습
- TD(Temporal Difference) 기반의 Reinforcement Learning
3) Reinforcement Learning 실습
- 간단한 2차원 게임에서의 실습 리뷰
http://www.fastcampus.co.kr/data_workshop_dla/
Intro to Reinforcement learning - part III
Introduction to Reinforcement Learning, part III: Basic approximate methods
This is the final presentation in a three-part series covering the basics of Reinforcement Learning (RL).
In this presentation, we introduce value function approximation and cover three different approaches to generating features for linear models.
We then take a sidestep to cover stochastic gradient descent in some detail before we return to introduce semi-gradient descent for RL. We also briefly cover a batch method as an alternative for episodic methods.
We discuss the implementation of the RL algorithms. For further discussion and illustrating the simulation results, we refer to Github repositories with source code of the implementation as well as Jupyter notebooks visualizing the simulation results.
Deep Reinforcement Learning
Deep Reinforcement Learning Talk at PI School. Covering following contents as:
1- Deep Reinforcement Learning
2- QLearning
3- Deep QLearning (DQN)
4- Google Deepmind Paper (DQN for ATARI)
Reinforcement Learning : A Beginners Tutorial
This a presentation of a Reinforcement Learning tutorial for beginners which I worked on.
Role of Tensors in Machine Learning
Tensors are higher order extensions of matrices that can incorporate multiple modalities and encode higher order relationships in data. Tensors play a significant role in machine learning through (1) tensor contractions, (2) tensor sketches, and (3) tensor decompositions. Tensor contractions are extensions of matrix products to higher dimensions. Tensor sketches efficiently compress tensors while preserving information. Tensor decompositions compute low rank components that constitute a tensor.
Planning in Artificial Intelligence
In the field of artificial intelligence (AI), planning refers to the process of developing a sequence of actions or steps that an intelligent agent should take to achieve a specific goal or solve a particular problem. AI planning is a fundamental component of many AI systems and has applications in various domains, including robotics, autonomous systems, scheduling, logistics, and more. Here are some key aspects of planning in AI:
Definition of Planning: Planning involves defining a problem, specifying the initial state, setting a goal state, and finding a sequence of actions or a plan that transforms the initial state into the desired goal state while adhering to certain constraints.
State-Space Representation: In AI planning, the problem is often represented as a state-space, where each state represents a snapshot of the system, and actions transform one state into another. The goal is to find a path through this state-space from the initial state to the goal state.
Search Algorithms: AI planning typically relies on search algorithms to explore the state-space efficiently. Uninformed search algorithms, such as depth-first search and breadth-first search, can be used, as well as informed search algorithms, like A* search, which incorporates heuristics to guide the search.
Heuristics: Heuristics are used in planning to estimate the cost or distance from a state to the goal. Heuristic functions help inform the search algorithms by providing an estimate of how close a state is to the solution. Good heuristics can significantly improve the efficiency of the search.
Plan Execution: Once a plan is generated, the next step is plan execution, where the agent carries out the actions in the plan to achieve the desired goal. This often requires monitoring the environment to ensure that the actions are executed as planned.
Temporal and Hierarchical Planning: In more complex scenarios, temporal planning deals with actions that have temporal constraints, and hierarchical planning involves creating plans at multiple levels of abstraction, making planning more manageable in complex domains.
Partial and Incremental Planning: Sometimes, it may not be necessary to create a complete plan from scratch. Partial and incremental planning allows agents to adapt and modify existing plans to respond to changing circumstances.
Applications: Planning is used in a wide range of applications, from manufacturing and logistics (e.g., scheduling production and delivery) to robotics (e.g., path planning for robots) and game playing (e.g., chess and video games).
Challenges: Challenges in AI planning include dealing with large search spaces, handling uncertainty, addressing resource constraints, and optimizing plans for efficiency and performance.
AI planning is a critical component in creating intelligent systems that can autonomously make decisions and solve complex problems.
Inteligencia Artificial
El procedimiento del museo británico; Ramificación y cota; El algoritmo A*. Procedimiento británico. ¿qué hacer para asegurar encontrar la ruta óptima?
Reinforcement learning and the Frozen Lake Problem
Slides from the Talk on Reinforcement Learning. We discussed the basic concepts behind RL. The Bellman Equation and used Value Iteration to solve the Frozen Lake problem.
Lecture 9 Markov decision process
It incorporates the fundamental of reinforcement learning, dynamic programming, and the Markov decision process.
Bayesian workflow with PyMC3 and ArviZ
Slides for my Talk at PyConDE/PyData Berlin 2019 https://de.pycon.org/program/pydata-rrlryg-a-bayesian-workflow-with-pymc-and-arviz-corrie-bartelheimer/
Resources:
Richard McElreath "Statistical Rethinking": https://xcelab.net/rm/statistical-rethinking/
Statistical Rethinking Port to PyMC3: https://github.com/pymc-devs/resources/tree/master/Rethinking
Prior Recommendation by Stan Team: https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations
Michael Betancourt Case Studies: https://betanalpha.github.io/writing/
Berlin Bayesian Meetup Group: https://www.meetup.com/de-DE/BerlinBayesians/
Code and Notebook:
https://github.com/corriebar/Bayesian-Workflow-with-PyMC
Deep Reinforcement Learning: Q-Learning
This slide reviews deep reinforcement learning, specially Q-Learning and its variants. We introduce Bellman operator and approximate it with deep neural network. Last but not least, we review the classical paper: DeepMind Atari Game beats human performance. Also, some tips of stabilizing DQN are included.
Survey on Haze Removal Techniques
This paper analyzed different haze removal methods. Haze causes trouble to
many computer graphics/vision applications as it reduces the visibility of the scene. Air light and
attenuation are two basic phenomena of haze. air light enhances the whiteness in scene and on
the other hand attenuation reduces the contrast. the colour and contrast of the scene is recovered
by haze removal techniques. many applications like object detection , surveillance, consumer
electronics etc. apply haze removal techniques. this paper widely focuses on the methods of
effectively eliminating haze from digital images. it also indicates the demerits of current
techniques.
Reinforcement Learning 4. Dynamic Programming
A summary of Chapter 4: Dynamic Programming 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
AI_Session 3 Problem Solving Agent and searching for solutions.pptx
AI_Session 3 Problem Solving Agent and searching for solutions
metodos de busqueda en inteligencia artificial
Cuadro comparativo sobre metodos de busqueda en inteligencia artificial
Reinforcement Learning Q-Learning
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
Making Complex Decisions(Artificial Intelligence)
This chapter shows how to use knowledge about the wlorld to make decisions even when the
outcomes of an action are uncertain and the rewards for acting might not be reaped until many
actions have passed. The main points are as follows:
e Sequential decision problems in uncertain envirsinments,also called Markov decision
processes, or MDPs, are defined by a transition model specifying the probabilistic
outcomes of actions and a reward function specifying the reward in each state.
o The utility of a state sequence is the sum of all the rewards over the sequence, possibly
discounted over time. The solution of an MDP is a policy that associates a decision
with every state that the agent might reach. An optimal policy maximizes the utility of
the state sequences encountered when it is execut~ed.
e The utility of a state is the expected utility of the state sequences encountered when
an optimal policy is executed, starting in that state. The value iteration algorithm for
solving MDPs works by iteratively solving the equations relating the utilities of each
state to that of its neighbors.
Policy iteration alternates between calculating the utilities of states under the current
policy and improving the current policy with respect to the current utilities.
* Partially observable MDPs, or POMDPs, are much more difficult to solve than are
MDPs. They can be solved by conversion to an MDP in the continuous space of belief
states. Optimal behavior in POMDPs includes information gathering to reduce uncertainty and therefore make better decisions in the fiuture.
A decision-theoretic agent can be constructed for POMDP environments. The agent
uses a dynamic decision network to represent the transition and observation models,
to update its belief state, and to project forward possible action sequences.
Game theory describes rational behavior for agents in situations where multiple agents
interact simultaneously. Solutions of games are Nash equilibria-strategy profiles in
which no agent has an incentive to deviate from the specified strategy.
Mechanism design can be used to set the rules by which agents will interact, in order
to maximize some global utility through the operation of individually rational agents.
Sometimes, mechanisms exist that achieve this goal without requiring each agent to
consider the choices made by other agents.
We shall return to the world of MDPs and POMDP in Chapter 21, when we study reinforcement learning methods that allow an agent to improve its behavior from experience in sequential, uncertain environments.
AI Lecture 5 (game playing)
cse 412 - artificial intelligence, Lectures by Tajim Md. Niamat Ullah Akhund.
Inteligencia Artificial y Sistemas Expertos
Universidad Central de Venezuela
UCV CEAP.
Maestría en Gerencia Empresarial
SISTEMAS DE INFORMACION EN LA EMPRESA
Más contenido relacionado
La actualidad más candente
Role of Tensors in Machine Learning
Tensors are higher order extensions of matrices that can incorporate multiple modalities and encode higher order relationships in data. Tensors play a significant role in machine learning through (1) tensor contractions, (2) tensor sketches, and (3) tensor decompositions. Tensor contractions are extensions of matrix products to higher dimensions. Tensor sketches efficiently compress tensors while preserving information. Tensor decompositions compute low rank components that constitute a tensor.
Planning in Artificial Intelligence
In the field of artificial intelligence (AI), planning refers to the process of developing a sequence of actions or steps that an intelligent agent should take to achieve a specific goal or solve a particular problem. AI planning is a fundamental component of many AI systems and has applications in various domains, including robotics, autonomous systems, scheduling, logistics, and more. Here are some key aspects of planning in AI:
Definition of Planning: Planning involves defining a problem, specifying the initial state, setting a goal state, and finding a sequence of actions or a plan that transforms the initial state into the desired goal state while adhering to certain constraints.
State-Space Representation: In AI planning, the problem is often represented as a state-space, where each state represents a snapshot of the system, and actions transform one state into another. The goal is to find a path through this state-space from the initial state to the goal state.
Search Algorithms: AI planning typically relies on search algorithms to explore the state-space efficiently. Uninformed search algorithms, such as depth-first search and breadth-first search, can be used, as well as informed search algorithms, like A* search, which incorporates heuristics to guide the search.
Heuristics: Heuristics are used in planning to estimate the cost or distance from a state to the goal. Heuristic functions help inform the search algorithms by providing an estimate of how close a state is to the solution. Good heuristics can significantly improve the efficiency of the search.
Plan Execution: Once a plan is generated, the next step is plan execution, where the agent carries out the actions in the plan to achieve the desired goal. This often requires monitoring the environment to ensure that the actions are executed as planned.
Temporal and Hierarchical Planning: In more complex scenarios, temporal planning deals with actions that have temporal constraints, and hierarchical planning involves creating plans at multiple levels of abstraction, making planning more manageable in complex domains.
Partial and Incremental Planning: Sometimes, it may not be necessary to create a complete plan from scratch. Partial and incremental planning allows agents to adapt and modify existing plans to respond to changing circumstances.
Applications: Planning is used in a wide range of applications, from manufacturing and logistics (e.g., scheduling production and delivery) to robotics (e.g., path planning for robots) and game playing (e.g., chess and video games).
Challenges: Challenges in AI planning include dealing with large search spaces, handling uncertainty, addressing resource constraints, and optimizing plans for efficiency and performance.
AI planning is a critical component in creating intelligent systems that can autonomously make decisions and solve complex problems.
Inteligencia Artificial
El procedimiento del museo británico; Ramificación y cota; El algoritmo A*. Procedimiento británico. ¿qué hacer para asegurar encontrar la ruta óptima?
Reinforcement learning and the Frozen Lake Problem
Slides from the Talk on Reinforcement Learning. We discussed the basic concepts behind RL. The Bellman Equation and used Value Iteration to solve the Frozen Lake problem.
Lecture 9 Markov decision process
It incorporates the fundamental of reinforcement learning, dynamic programming, and the Markov decision process.
Bayesian workflow with PyMC3 and ArviZ
Slides for my Talk at PyConDE/PyData Berlin 2019 https://de.pycon.org/program/pydata-rrlryg-a-bayesian-workflow-with-pymc-and-arviz-corrie-bartelheimer/
Resources:
Richard McElreath "Statistical Rethinking": https://xcelab.net/rm/statistical-rethinking/
Statistical Rethinking Port to PyMC3: https://github.com/pymc-devs/resources/tree/master/Rethinking
Prior Recommendation by Stan Team: https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations
Michael Betancourt Case Studies: https://betanalpha.github.io/writing/
Berlin Bayesian Meetup Group: https://www.meetup.com/de-DE/BerlinBayesians/
Code and Notebook:
https://github.com/corriebar/Bayesian-Workflow-with-PyMC
Deep Reinforcement Learning: Q-Learning
This slide reviews deep reinforcement learning, specially Q-Learning and its variants. We introduce Bellman operator and approximate it with deep neural network. Last but not least, we review the classical paper: DeepMind Atari Game beats human performance. Also, some tips of stabilizing DQN are included.
Survey on Haze Removal Techniques
This paper analyzed different haze removal methods. Haze causes trouble to
many computer graphics/vision applications as it reduces the visibility of the scene. Air light and
attenuation are two basic phenomena of haze. air light enhances the whiteness in scene and on
the other hand attenuation reduces the contrast. the colour and contrast of the scene is recovered
by haze removal techniques. many applications like object detection , surveillance, consumer
electronics etc. apply haze removal techniques. this paper widely focuses on the methods of
effectively eliminating haze from digital images. it also indicates the demerits of current
techniques.
Reinforcement Learning 4. Dynamic Programming
A summary of Chapter 4: Dynamic Programming 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
AI_Session 3 Problem Solving Agent and searching for solutions.pptx
AI_Session 3 Problem Solving Agent and searching for solutions
metodos de busqueda en inteligencia artificial
Cuadro comparativo sobre metodos de busqueda en inteligencia artificial
Reinforcement Learning Q-Learning
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
Making Complex Decisions(Artificial Intelligence)
This chapter shows how to use knowledge about the wlorld to make decisions even when the
outcomes of an action are uncertain and the rewards for acting might not be reaped until many
actions have passed. The main points are as follows:
e Sequential decision problems in uncertain envirsinments,also called Markov decision
processes, or MDPs, are defined by a transition model specifying the probabilistic
outcomes of actions and a reward function specifying the reward in each state.
o The utility of a state sequence is the sum of all the rewards over the sequence, possibly
discounted over time. The solution of an MDP is a policy that associates a decision
with every state that the agent might reach. An optimal policy maximizes the utility of
the state sequences encountered when it is execut~ed.
e The utility of a state is the expected utility of the state sequences encountered when
an optimal policy is executed, starting in that state. The value iteration algorithm for
solving MDPs works by iteratively solving the equations relating the utilities of each
state to that of its neighbors.
Policy iteration alternates between calculating the utilities of states under the current
policy and improving the current policy with respect to the current utilities.
* Partially observable MDPs, or POMDPs, are much more difficult to solve than are
MDPs. They can be solved by conversion to an MDP in the continuous space of belief
states. Optimal behavior in POMDPs includes information gathering to reduce uncertainty and therefore make better decisions in the fiuture.
A decision-theoretic agent can be constructed for POMDP environments. The agent
uses a dynamic decision network to represent the transition and observation models,
to update its belief state, and to project forward possible action sequences.
Game theory describes rational behavior for agents in situations where multiple agents
interact simultaneously. Solutions of games are Nash equilibria-strategy profiles in
which no agent has an incentive to deviate from the specified strategy.
Mechanism design can be used to set the rules by which agents will interact, in order
to maximize some global utility through the operation of individually rational agents.
Sometimes, mechanisms exist that achieve this goal without requiring each agent to
consider the choices made by other agents.
We shall return to the world of MDPs and POMDP in Chapter 21, when we study reinforcement learning methods that allow an agent to improve its behavior from experience in sequential, uncertain environments.
AI Lecture 5 (game playing)
cse 412 - artificial intelligence, Lectures by Tajim Md. Niamat Ullah Akhund.
La actualidad más candente (20)
Reinforcement learning and the Frozen Lake Problem
Reinforcement learning and the Frozen Lake Problem
AI_Session 3 Problem Solving Agent and searching for solutions.pptx
AI_Session 3 Problem Solving Agent and searching for solutions.pptx
Destacado
Inteligencia Artificial y Sistemas Expertos
Universidad Central de Venezuela
UCV CEAP.
Maestría en Gerencia Empresarial
SISTEMAS DE INFORMACION EN LA EMPRESA
Inteligencia Artificial y Sistemas Expertos
Esta es una presentación donde encontrarás conceptos básicos, características, etapas, entre otras, sobre Inteligencia Artificial y Sistemas Expertos. Realizado por Estudiantes del PG en Mercadeo para Empresas de la UCV. Venezuela.
El conocimiento en Inteligencia Artificial
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Destacado (6)
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aprendizaje por refuerzo
- 1. UNIVERSIDAD TECNICA PARTICULAR DE LOJA ESCUELA DE LAS CIENCIAS DE LA COMPUTACIÓN
- 6. Modelo de Aprendizaje por Refuerzo.
- 10. Aplicación Modelo del percepptron
- 13. Resultados La corrida nos indica, el perceptron tardará hasta 38 veces de entrenamiento para tener un aprendizaje óptimo.