Yahoo Web Search

Search results

1. Top results related to reinforcement learning

   • Reinforcement Learning or Supervised Learning?

     Top Answer

     Answered Nov 14, 2018 · 5 votes

     The reason is because the two fields has a fundamental difference:

     One tries to replicate previous results and the other tries to be better than previous results.

     There are 4 fields in machine learning:

     • Supervised learning
     • Unsupervised Learning
     • Semi-supervised Learning
     • Reinforcement learning

     Let's talking about the two fields you asked for, and let's intuitively explore them with a real life example of archery.

     Supervised Learning

     For supervised learning, we would observe a master archer in action for maybe a week and record how far they pulled the bow string back, angle of shot, etc. And then we go home and build a model. In the most ideal scenario, our model becomes equally as good as the master archer. It cannot get better because the loss function in supervised learning is usually MSE or Cross entropy, so we simply try to replicate the feature label mapping. After building the model, we deploy it. And let's just say we're extra fancy and make it learn online. So we continually take data from the master archer and continue to learn to be exactly the same as the master archer.

     The biggest takeaway:

     We're trying to replicate the master archer simply because we think he is the best. Therefore we can never beat him.

     Reinforcement Learning

     In reinforcement learning, we simply build a model and let it try many different things. And we give it a reward / penalty depending on how far the arrow was from the bullseye. We are not trying to replicate any behaviour, instead, we try to find our own optimal behaviour. Because of this, we are not given any bias towards what we think the optimal shooting strategy is.

     Because RL does not have any prior knowledge, it may be difficult for RL to converge on difficult problems. Therefore, there is a method called apprenticeship learning / imitation learning, where we basically give the RL some trajectories of master archers just so it can have a starting point and begin to converge. But after that, RL will explore by taking random actions sometimes to try to find other optimal solutions. This is something that supervised learning cannot do. Because if you explore using supervised learning, you are basically saying by taking this action in this state is optimal. Then you try to make your model replicate it. But this scenario is wrong in supervised learning, and should instead be seen as an outlier in the data.

     Key differences of Supervised learning vs RL:

     • Supervised Learning replicates what's already done
     • Reinforcement learning can explore the state space, and do random actions. This then allows RL to be potentially better than the current best.

     Why we don’t use the same simulated environment to generate the labeled data and then use supervised learning methods instead of RL

     We do this for Deep RL because it has an experience replay buffer. But this is not possible for supervised learning because the concept of reward is lacking.

     Example: Walking in a maze.

     Reinforcement Learning

     Taking a right in square 3: Reward = 5

     Taking a left in square 3: Reward = 0

     Taking a up in square 3: Reward = -5

     Supervised Learning

     Taking a right in square 3

     Taking a left in square 3

     Taking a up in square 3

     When you try to make a decision in square 3, RL will know to go right. Supervised learning will be confused, because in one example, your data said to take a right in square 3, 2nd example says to take left, 3rd example says to go up. So it will never converge.

     Check all answers on Stack Overflow

     1/5

   • Reinforcement learning

     Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Reinforcement learning differs from supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience. 

     Reinforcement Learning (RL) is the science of decision making. It is about learning the optimal behavior in an environment to obtain maximum reward. In RL, the data is accumulated from machine learning systems that use a trial-and-error method. Data is not part of the input that we would find in supervised or unsupervised machine learning.

     Reinforcement learning uses algorithms that learn from outcomes and decide which action to take next. After each action, the algorithm...

     Check all answers on Geeksforgeeks

     2/5

   • Reinforcement learning

     Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Reinforcement learning differs from supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience. 

     Reinforcement Learning (RL) is the science of decision making. It is about learning the optimal behavior in an environment to obtain maximum reward. In RL, the data is accumulated from machine learning systems that use a trial-and-error method. Data is not part of the input that we would find in supervised or unsupervised machine learning.

     Reinforcement learning uses algorithms that learn from outcomes and decide which action to take next. After each action, the algorithm...

     Check all answers on Geeksforgeeks

     3/5

   • Reinforcement learning

     Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Reinforcement learning differs from supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience. 

     Reinforcement Learning (RL) is the science of decision making. It is about learning the optimal behavior in an environment to obtain maximum reward. In RL, the data is accumulated from machine learning systems that use a trial-and-error method. Data is not part of the input that we would find in supervised or unsupervised machine learning.

     Reinforcement learning uses algorithms that learn from outcomes and decide which action to take next. After each action, the algorithm...

     Check all answers on Geeksforgeeks

     4/5

   • Also try

     reinforcement learning in machine learning	reinforcement learning algorithms
     deep reinforcement learning	types of reinforcement learning
     reinforcement learning example	reinforcement learning meaning
     supervised unsupervised reinforcement learning	reinforcement learning in ai

     5/5

   Show moreShow less

2. en.wikipedia.org › wiki › Reinforcement_learningReinforcement learning - Wikipedia

   en.wikipedia.org › wiki › Reinforcement_learning

   • Cached

   Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward.

3. www.geeksforgeeks.org › what-is-reinforcement-learningReinforcement learning - GeeksforGeeks

   www.geeksforgeeks.org › what-is-reinforcement-learning

   • Cached

   Sep 4, 2024 · Reinforcement Learning (RL) is a branch of machine learning focused on making decisions to maximize cumulative rewards in a given situation. Unlike supervised learning, which relies on a training dataset with predefined answers, RL involves learning through experience.

   • Video Duration: 7 min

4. www.ibm.com › topics › reinforcement-learningWhat is reinforcement learning? - IBM

   www.ibm.com › topics › reinforcement-learning

   • Cached

   In reinforcement learning, an autonomous agent learns to perform a task by trial and error in the absence of any guidance from a human user. 1 It particularly addresses sequential decision-making problems in uncertain environments, and shows promise in artificial intelligence development. Supervised and unsupervised learning.

5. towardsdatascience.com › reinforcement-learningReinforcement Learning 101. Learn the essentials of ...

   towardsdatascience.com › reinforcement-learning

   Mar 19, 2018 · Reinforcement Learning (RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences.

   • Author: Shweta Bhatt

6. web.stanford.edu › class › psych209Reinforcement Learning: An Introduction - Stanford University

   web.stanford.edu › class › psych209

   a learning system that wants something, that adapts its behavior in order to maximize a special signal from its environment. This was the idea of a \he-donistic" learning system, or, as we would say now, the idea of reinforcement learning. Like others, we had a sense that reinforcement learning had been thor-

7. aws.amazon.com › what-is › reinforcement-learningWhat is Reinforcement Learning? - Reinforcement Learning ...

   aws.amazon.com › what-is › reinforcement-learning

   • Cached

   Reinforcement learning (RL) is a machine learning (ML) technique that trains software to make decisions to achieve the most optimal results. It mimics the trial-and-error learning process that humans use to achieve their goals.

8. builtin.com › reinforcement-learningWhat Is Reinforcement Learning? (Definition, Uses) | Built In

   builtin.com › reinforcement-learning

   • Cached

   Aug 31, 2023 · Reinforcement learning is a training method in machine learning where an algorithm or agent completes a task through trial and error. An agent must explore a controlled environment and learn from its actions the optimal way to achieve a certain goal.

9. People also ask

   What is reinforcement learning (RL)?

   • Reinforcement learning ( RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward.

   Reinforcement learning - Wikipedia

   en.wikipedia.org/wiki/Reinforcement_learning

   See all results for this question

   What is reinforcement learning?

   • Reinforcement learning is a technique for solving Markov decision problems. Reinforcement learning uses a formal framework defining the interaction between a learning agent and its environment in terms of states, actions, and rewards.

   Reinforcement learning - GeeksforGeeks

   www.geeksforgeeks.org/what-is-reinforcement-learning/

   See all results for this question

   What is reinforcement learning ML?

   • 1. What is Reinforcement Learning? How does it compare with other ML techniques? Reinforcement Learning (RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences.

   Reinforcement Learning 101. Learn the essentials of Reinforcement… | by

   towardsdatascience.com/reinforcement-learning-101-e24b50e1d292

   See all results for this question

   What is reinforcement learning in robotics?

   • In reinforcement learning, an agent learns to make decisions by interacting with an environment. It is used in robotics and other decision-making settings. Reinforcement learning (RL) is a type of machine learning process that focuses on decision making by autonomous agents.

   What is reinforcement learning? | IBM

   www.ibm.com/topics/reinforcement-learning

   See all results for this question