Page Comparison

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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)

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

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