Reinforcement Learning (RL) approaches to deal with finding an optimal reward based policy to act in an environment (Charla en Inglés)
However, what has led to their widespread use is its combination with deep neural networks (DNN) i.e., deep reinforcement learning (Deep RL). Recent successes on not only learning to play games but also superseding humans in it and academia-industry research collaborations like for manipulation of objects, locomotion skills, smart grids, etc. have surely demonstrated their case on a wide variety of challenging tasks.
With application spanning across games, robotics, dialogue, healthcare, marketing, energy and many more domains, Deep RL might just be the power that drives the next generation of Artificial Intelligence (AI) agents!
This presentation contains an introduction to reinforcement learning, comparison with others learning ways, introduction to Q-Learning and some applications of reinforcement learning in video games.
Reinforcement Learning (RL) approaches to deal with finding an optimal reward based policy to act in an environment (Charla en Inglés)
However, what has led to their widespread use is its combination with deep neural networks (DNN) i.e., deep reinforcement learning (Deep RL). Recent successes on not only learning to play games but also superseding humans in it and academia-industry research collaborations like for manipulation of objects, locomotion skills, smart grids, etc. have surely demonstrated their case on a wide variety of challenging tasks.
With application spanning across games, robotics, dialogue, healthcare, marketing, energy and many more domains, Deep RL might just be the power that drives the next generation of Artificial Intelligence (AI) agents!
This presentation contains an introduction to reinforcement learning, comparison with others learning ways, introduction to Q-Learning and some applications of reinforcement learning in video games.
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
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)
In this slide we have coverd the following topices
What is Artificial Intelligence?
What is Machine Learning?
Relationship among AI, ML and DL.
Human Brain Learning Process
Learning Vs Recognition
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Definition of Reinforcement Learning
Reinforcement Learning Application: AWS Deep racer
Markov Decision Process
Understanding Q-Learning Algorithm
Q-Learning Algorithm Example
Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.
In this talk we discuss about the aplicação of Reinforcement Learning to Games. Recently, OpenAI created an algorithm capable of beating a human team in DOTA, considered a game with great amount of complexity and strategy. In this talk, we'll evaluate the role Reinforcement Learning plays in the world of games, taking a look at some of main achievements and how they look like in terms of implementation. We'll also take a look at some of the history of AI applied to games and how things evolved over time.
MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL) by Lex FridmanPeerasak C.
MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL) by Lex Fridman
Watch video: https://youtu.be/zR11FLZ-O9M
First lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.
INFO:
Website: https://deeplearning.mit.edu
CONNECT:
- If you enjoyed this video, please subscribe to this channel.
- Twitter: https://twitter.com/lexfridman
- LinkedIn: https://www.linkedin.com/in/lexfridman
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Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep L...Simplilearn
Deep Learning covers all the essential Deep Learning frameworks that are necessary to build AI models. In this presentation, you will learn about the development of essential frameworks such as TensorFlow, Keras, PyTorch, Theano, etc. You will also understand the programming languages used to build the frameworks, the different companies that use these frameworks, the characteristics of these Deep Learning frameworks, and type of models that were built using these frameworks. Now, let us get started with understanding the different popular Deep Learning frameworks being used in industries.
Below are the different Deep Learning frameworks we'll be discussing in this presentation:
1. TensorFlow
2. Keras
3. PyTorch
4. Theano
5. Deep Learning 4 Java
6. Caffe
7. Chainer
8. Microsoft CNTK
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
Deep Reinforcement Learning and Its ApplicationsBill Liu
What is the most exciting AI news in recent years? AlphaGo!
What are key techniques for AlphaGo? Deep learning and reinforcement learning (RL)!
What are application areas for deep RL? A lot! In fact, besides games, deep RL has been making tremendous achievements in diverse areas like recommender systems and robotics.
In this talk, we will introduce deep reinforcement learning, present several applications, and discuss issues and potential solutions for successfully applying deep RL in real life scenarios.
https://www.aicamp.ai/event/eventdetails/W2021042818
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
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)
In this slide we have coverd the following topices
What is Artificial Intelligence?
What is Machine Learning?
Relationship among AI, ML and DL.
Human Brain Learning Process
Learning Vs Recognition
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Definition of Reinforcement Learning
Reinforcement Learning Application: AWS Deep racer
Markov Decision Process
Understanding Q-Learning Algorithm
Q-Learning Algorithm Example
Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.
In this talk we discuss about the aplicação of Reinforcement Learning to Games. Recently, OpenAI created an algorithm capable of beating a human team in DOTA, considered a game with great amount of complexity and strategy. In this talk, we'll evaluate the role Reinforcement Learning plays in the world of games, taking a look at some of main achievements and how they look like in terms of implementation. We'll also take a look at some of the history of AI applied to games and how things evolved over time.
MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL) by Lex FridmanPeerasak C.
MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL) by Lex Fridman
Watch video: https://youtu.be/zR11FLZ-O9M
First lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.
INFO:
Website: https://deeplearning.mit.edu
CONNECT:
- If you enjoyed this video, please subscribe to this channel.
- Twitter: https://twitter.com/lexfridman
- LinkedIn: https://www.linkedin.com/in/lexfridman
- Facebook: https://www.facebook.com/lexfridman
- Instagram: https://www.instagram.com/lexfridman
Deep Learning Frameworks 2019 | Which Deep Learning Framework To Use | Deep L...Simplilearn
Deep Learning covers all the essential Deep Learning frameworks that are necessary to build AI models. In this presentation, you will learn about the development of essential frameworks such as TensorFlow, Keras, PyTorch, Theano, etc. You will also understand the programming languages used to build the frameworks, the different companies that use these frameworks, the characteristics of these Deep Learning frameworks, and type of models that were built using these frameworks. Now, let us get started with understanding the different popular Deep Learning frameworks being used in industries.
Below are the different Deep Learning frameworks we'll be discussing in this presentation:
1. TensorFlow
2. Keras
3. PyTorch
4. Theano
5. Deep Learning 4 Java
6. Caffe
7. Chainer
8. Microsoft CNTK
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations, and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
Deep Reinforcement Learning and Its ApplicationsBill Liu
What is the most exciting AI news in recent years? AlphaGo!
What are key techniques for AlphaGo? Deep learning and reinforcement learning (RL)!
What are application areas for deep RL? A lot! In fact, besides games, deep RL has been making tremendous achievements in diverse areas like recommender systems and robotics.
In this talk, we will introduce deep reinforcement learning, present several applications, and discuss issues and potential solutions for successfully applying deep RL in real life scenarios.
https://www.aicamp.ai/event/eventdetails/W2021042818
Document contains some of the questions from the Domingos Paper. Overall idea is to understand what Machine Learning is all about. This paper helps us to understand the need of Machine Learning in our day to day lives. Well I you will find this document helpful.
Machine Learning Interview Questions and AnswersSatyam Jaiswal
Practice Best Machine Learning Interview Questions and Answers for the best preparation of the machine learning interview. these questions are very popular and asked various times in machine learning interview.
In a world of data explosion, the rate of data generation and consumption is on the increasing side,
there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
2. Reinforcement learning What is it and why is it important in machine learning? What machine learning algorithms exist for it? Q-learning in theory How does it work? How can it be improved? Q-learning in practice What are the challenges? What are the applications? Link with psychology Do people use similar mechanisms? Do people use other methods that could inspire algorithms? Resources for future reference Outline
3. Reinforcement learning What is it and why is it important in machine learning? What machine learning algorithms exist for it? Q-learning in theory How does it work? How can it be improved? Q-learning in practice What are the challenges? What are the applications? Link with psychology Do people use similar mechanisms? Do people use other methods that could inspire algorithms? Resources for future reference Outline
4. Machine Learning Classification: where AI meets statistics Given Training data Learn A model for making a single prediction or decision xnew Classification Algorithm Training Data (x1, y1) (x2, y2) (x3, y3) … Model ynew
6. Learning how to act to accomplish goals Given Environment that contains rewards Learn A policy for acting Important differences from classification You don’t get examples of correct answers You have to try things in order to learn Procedural Learning
8. Do you know your environment? The effects of actions The rewards If yes, you can use Dynamic Programming More like planning than learning Value Iteration and Policy Iteration If no, you can use Reinforcement Learning (RL) Acting and observing in the environment What You Know Matters
9. RL shapes behavior using reinforcement Agent takes actions in an environment (in episodes) Those actions change the state and trigger rewards Through experience, an agent learns a policy for acting Given a state, choose an action Maximize cumulative reward during an episode Interesting things about this problem Requires solving credit assignment What action(s) are responsible for a reward? Requires both exploring and exploiting Do what looks best, or see if something else is really best? RL as Operant Conditioning
10. Search-based: evolution directly on a policy E.g. genetic algorithms Model-based: build a model of the environment Then you can use dynamic programming Memory-intensive learning method Model-free: learn a policy without any model Temporal difference methods (TD) Requires limited episodic memory (though more helps) Types of Reinforcement Learning
11. Actor-critic learning The TD version of Policy Iteration Q-learning The TD version of Value Iteration This is the most widely used RL algorithm Types of Model-Free RL
12. Reinforcement learning What is it and why is it important in machine learning? What machine learning algorithms exist for it? Q-learning in theory How does it work? How can it be improved? Q-learning in practice What are the challenges? What are the applications? Link with psychology Do people use similar mechanisms? Do people use other methods that could inspire algorithms? Resources for future reference Outline
13. Current state: s Current action: a Transition function: δ(s, a) = sʹ Reward function: r(s, a) Є R Policy π(s) = a Q(s, a) ≈ value of taking action a from state s Q-Learning: Definitions Markov property: this is independent of previous states given current state In classification we’d have examples (s, π(s)) to learn from
14. Q(s, a) estimates the discounted cumulative reward Starting in state s Taking action a Following the current policy thereafter Suppose we have the optimal Q-function What’s the optimal policy in state s? The action argmaxb Q(s, b) But we don’t have the optimal Q-function at first Let’s act as if we do And updates it after each step so it’s closer to optimal Eventually it will be optimal! The Q-function
21. The Need for Exploration 1 2 3 Explore! 4 5 6 7 8 9 10 11
22. Can’t always choose the action with highest Q-value The Q-function is initially unreliable Need to explore until it is optimal Most common method: ε-greedy Take a random action in a small fraction of steps (ε) Decay ε over time There is some work on optimizing exploration Kearns & Singh, ML 1998 But people usually use this simple method Explore/Exploit Tradeoff
23. Under certain conditions, Q-learning will converge to the correct Q-function The environment model doesn’t change States and actions are finite Rewards are bounded Learning rate decays with visits to state-action pairs Exploration method would guarantee infinite visits to every state-action pair over an infinite training period Q-Learning: Convergence
24.
25. Use the action actually chosen in updatesRegular: PIT! SARSA:
26.
27. Use some episodic memory to speed credit assignment1 2 3 4 5 6 7 8 9 10 11 TD(λ): a weighted combination of look-ahead distances The parameter λ controls the weighting
28. Eligibility traces: Lookahead with less memory Visiting a state leaves a trace that decays Update multiple states at once States get credit according to their trace Extensions: Eligibility Traces 3 1 2 4 5 6 9 7 8 10 11
29.
30. Extensions: Function Approximation Function approximation: allow complex environments The Q-function table could be too big (or infinitely big!) Describe a state by a feature vector f = (f1 , f2 , … , fn) Then the Q-function can be any regression model E.g. linear regression: Q(s, a) = w1 f1 + w2 f2 + … + wn fn Cost: convergence goes away in theory, though often not in practice Benefit: generalization over similar states Easiest if the approximator can be updated incrementally, like neural networks with gradient descent, but you can also do this in batches
31. Reinforcement learning What is it and why is it important in machine learning? What machine learning algorithms exist for it? Q-learning in theory How does it work? How can it be improved? Q-learning in practice What are the challenges? What are the applications? Link with psychology Do people use similar mechanisms? Do people use other methods that could inspire algorithms? Resources for future reference Outline
32. Feature/reward design can be very involved Online learning (no time for tuning) Continuous features(handled by tiling) Delayed rewards (handled by shaping) Parameters can have large effects on learning speed Tuning has just one effect: slowing it down Realistic environments can have partial observability Realistic environments can be non-stationary There may be multiple agents Challenges in Reinforcement Learning
33. Tesauro 1995: Backgammon Crites & Barto 1996: Elevator scheduling Kaelbling et al. 1996: Packaging task Singh & Bertsekas 1997: Cell phone channel allocation Nevmyvaka et al. 2006: Stock investment decisions Ipek et al. 2008: Memory control in hardware Kosorok 2009: Chemotherapy treatment decisions No textbook “killer app” Just behind the times? Too much design and tuning required? Training too long or expensive? Too much focus on toy domains in research? Applications of Reinforcement Learning
34. Reinforcement learning What is it and why is it important in machine learning? What machine learning algorithms exist for it? Q-learning in theory How does it work? How can it be improved? Q-learning in practice What are the challenges? What are the applications? Link with psychology Do people use similar mechanisms? Do people use other methods that could inspire algorithms? Resources for future reference Outline
35. Should machine learning researchers care? Planes don’t fly the way birds do; should machines learn the way people do? But why not look for inspiration? Psychological research does show neuron activity associated with rewards Really prediction error: actual – expected Primarily in the striatum Do Brains Perform RL?
36. Schönberg et al., J. Neuroscience 2007 Good learners have stronger signals in the striatum than bad learners Frank et al., Science 2004 Parkinson’s patients learn better from negatives On dopamine medication, they learn better from positives Bayer & Glimcher, Neuron 2005 Average firing rate corresponds to positive prediction errors Interestingly, not to negative ones Cohen & Ranganath, J. Neuroscience 2007 ERP magnitude predicts whether subjects change behavior after losing Support for Reward Systems
37. Various results in animals support different algorithms Montague et al., J. Neuroscience 1996: TD O’Doherty et al., Science 2004: Actor-critic Daw, Nature 2005: Parallel model-free and model-based Morris et al., Nature 2006: SARSA Roesch et al., Nature 2007: Q-learning Other results support extensions Bogacz et al., Brain Research 2005: Eligibility traces Daw, Nature 2006: Novelty bonuses to promote exploration Mixed results on reward discounting (short vs. long term) Ainslie 2001: people are more impulsive than algorithms McClure et al., Science 2004: Two parallel systems Frank et al., PNAS 2007: Controlled by genetic differences Schweighofer et al., J. Neuroscience 2008: Influenced by serotonin Support for Specific Mechanisms
38. Parallelism Separate systems for positive/negative errors Multiple algorithms running simultaneously Use of RL in combination with other systems Planning: Reasoning about why things do or don’t work Advice: Someone to imitate or correct us Transfer: Knowledge about similar tasks More impulsivity Is this necessarily better? The goal for machine learning: Take inspiration from humans without being limited by their shortcomings What People Do Better My work
39. Reinforcement LearningSutton & Barto, MIT Press 1998 The standard reference book on computational RL Reinforcement LearningDayan, Encyclopedia of Cognitive Science 2001 A briefer introduction that still touches on many computational issues Reinforcement learning: the good, the bad, and the uglyDayan & Niv, Current Opinions in Neurobiology 2008 A comprehensive survey of work on RL in the human brain Resources on Reinforcement Learning