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NameML Category
Jahanvi                                  Supervised                                        
AkankshaUnsupervised
Kanak RajReinforced


Supervised

  • Supervised learning algorithms make predictions based on a set of examples
  • Classification: When the data are being used to predict a categorical variable, supervised learning is also called classification. This is the case when assigning a label or indicator, either dog or cat to an image. When there are only two labels, this is called binary classification. When there are more than two categories, the problems are called multi-class classification.
  • Regression: When predicting continuous values, the problems become a regression problem.
  • Forecasting: This is the process of making predictions about the future based on past and present data. It is most commonly used to analyze trends. A common example might be an estimation of the next year sales based on the sales of the current year and previous years.

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Algorithms

In progress**

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NameComments on ApplicabilityReference









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  1. Active Learning
  2. No labeled data
  3. No supervisor, only  reward
  4. Actions are sequential
  5. Feedback is delayed, not instantaneous.
  6. Can afford to make mistakes?
  7. Is it possible to use a simulated environment for the task?
  8. Lots of time
  9. Think about the variables that can define the state of the environment.
    1. State Variables and Quantify them
    2. The agent has access to these variables at every time step
    3. Concrete Reward Function and Compute Reward after action
    4. Define Policy Function

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Model-Free vs Model-Based RL

Whether the agent has access to (or learns) a model of the environment(a function that predicts state transitions and rewards)

Model Free

Model-Based

 forego the potential gains in sample efficiency from using a model

Allows to plan ahead and look in possible results for a range of possible choices.

 easier to implement and tune.

Ground Truth Model for any task is generally not available.


If agents want to use a model then it has to prepare it purely from experience


fundamentally hard


being willing to throw lots of time 


High computation


Can fail off due to over-exploitation of bias

What to Learn in Model-Free RL

  1. Policy Optimization
  2. Q-Learning

    Policy Optimization

    Q-Learning

    optimize the parameters either directly by gradient ascent on the performance objective or indirectly, by maximizing local approximations

    learn an approximator for the optimal action-value function

    performed on-policy, each update only uses data collected while acting according to the most recent version of the policy

    performed off-policy, each update can use data collected at any point during training

    directly optimize for the thing you want

    indirectly optimize for agent performance

    More stable

    tends to be less stable

    advantage of being substantially more sample efficient when they do work, because they can reuse data more effectively 

    Less sample efficient and takes longer to learn as learning data is limited at every iteration.




Algorithms

NameComments on ApplicabilityReference

Q Learning









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