Contents
- 1 What is the difference between on policy and off-policy learning?
- 2 Why is offline learning better than online learning?
- 3 When to use off policy or on policy reinforcement learning?
- 4 Which is the most fundamental idea in reinforcement learning?
- 5 How are reinforcement learning models used for hyperparameter optimization?
What is the difference between on policy and off-policy learning?
The difference is this: In on-policy learning, the Q(s,a) function is learned from actions that we took using our current policy π(a|s). In off-policy learning, the Q(s,a) function is learned from taking different actions (for example, random actions).
Why is offline learning better than online learning?
Ans. The main advantage of the offline study is that it has developed more understanding methods as compared to online study. The combination of teachers and students present in class provides comfortable ability to understand the topic in a better way.
What is a policy in reinforcement learning talks about?
A policy is, therefore, a strategy that an agent uses in pursuit of goals. The policy dictates the actions that the agent takes as a function of the agent’s state and the environment.
When to use off policy or on policy reinforcement learning?
On-policy reinforcement learning is useful when you want to optimize the value of an agent that is exploring. For offline learning, where the agent does not explore much, off-policy RL may be more appropriate. For instance, off-policy classification is good at predicting movement in robotics.
Which is the most fundamental idea in reinforcement learning?
Temporal Difference (TD) algorithms — A class of learning methods, based on the idea of comparing temporally successive predictions. Possibly the single most fundamental idea in all of reinforcement learning.
What’s the difference between reinforcement learning and supervised learning?
What are difference between Reinforcement Learning (RL) and Supervised Learning? The main difference is to do with how “correct” or optimal results are learned: In Supervised Learning, the learning model is presented with an input and desired output. It learns by example.
How are reinforcement learning models used for hyperparameter optimization?
Comparing reinforcement learning models for hyperparameter optimization is an expensive affair, and often practically infeasible. So the performance of these algorithms is evaluated via on-policy interactions with the target environment.