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1 vote

"Unique" or "Repeated" experiences in memory replay...?

This question is related to some unresolved issues in reinforcement learning with approximation. The following is a partial answer, but is the advice you should initially follow, unless you have a ...
Neil Slater's user avatar
  • 32.5k
1 vote

What is the difference between Q-learning, Deep Q-learning and Deep Q-network?

Q-learning Q-learning is a basic reinforcement learning algorithm. It uses a Q-table to store and update the value of each state-action pair. The algorithm updates the Q-values using the Bellman ...
Kristina_Poole's user avatar
1 vote

tic-tac-toe - tabular q-learning - what is the formula to calculate the number of entries in the q-table

Great answer from @Neil Slater; however, I wanted to give my 2 cents. Even though it's called "tabular" q-learning, it doesn't imply that you have to use a table to access it; yes, a table ...
Alberto's user avatar
  • 2,248
1 vote
Accepted

tic-tac-toe - tabular q-learning - what is the formula to calculate the number of entries in the q-table

The bad news is that there is no simple formula for calculating the state space or state+action space here. It is relatively easy to get upper or lower bounds to it, but complex to precisely calculate....
Neil Slater's user avatar
  • 32.5k
2 votes

Why is there no exploration-optimality trade-off in Q-learning?

Whilst it's not perfect separation, because you may still pay real costs for exploration during learning (in fact possibly worse than on-policy methods), then off-policy methods do offer a clean way ...
Neil Slater's user avatar
  • 32.5k
2 votes

Why is there no exploration-optimality trade-off in Q-learning?

I feel that this question is more easily understood by contrasting on-policy methods with off-policy methods. An off-policy method generates data in the environment with a behavior policy during ...
DeepQZero's user avatar
  • 1,399

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