When immigrate from DP to MDP and when we immigrate from a MDP (Q-learning) to DQN?
$begingroup$
In the following of my previous question : Difference between DP and Q-learning?
When we immigrate from DP to MDP and when we immigrate from a MDP (Q-learning) to DQN?
I have some problem in the reason of immigration from DP to a MDP like Q-learning and from Q-learning to DQN? It seems to be so similar what is the difference of the reasons?
Please check the validity of my sentences or correct it:
If there are 5 variables in each time step which each of them could give one of 3 possible value and it is running for 100 time steps, Then making all pairs of states which is equivalent to the filling completely the Q-learning in running time steps is not possible ( #states=(5 ^ 3) ^ 100), then it is essential to migrate from QL to DQN.
=> We are able to say that the enormous states leads to immigrate from Q-learning to DQN? Is it right?
On contrast, DP is immigrated to RL when probability of observing the next state and reward based on the present state and action is not available. This reason seems to be the same and large enormous states leads to immigrate from DP to MDP?
What is the difference between these two types of the immigration:
DP(value-iteration)->RL(QL) and RL(QL)->DQN?
Why the reason seems to be equivalent? What is the difference?
What is wrong in my clarification?
reinforcement-learning q-learning dqn
asked 20 mins ago
user10296606user10296606
1937
$endgroup$
add a comment |
$begingroup$
In the following of my previous question : Difference between DP and Q-learning?
When we immigrate from DP to MDP and when we immigrate from a MDP (Q-learning) to DQN?
I have some problem in the reason of immigration from DP to a MDP like Q-learning and from Q-learning to DQN? It seems to be so similar what is the difference of the reasons?
Please check the validity of my sentences or correct it:
If there are 5 variables in each time step which each of them could give one of 3 possible value and it is running for 100 time steps, Then making all pairs of states which is equivalent to the filling completely the Q-learning in running time steps is not possible ( #states=(5 ^ 3) ^ 100), then it is essential to migrate from QL to DQN.
=> We are able to say that the enormous states leads to immigrate from Q-learning to DQN? Is it right?
On contrast, DP is immigrated to RL when probability of observing the next state and reward based on the present state and action is not available. This reason seems to be the same and large enormous states leads to immigrate from DP to MDP?
What is the difference between these two types of the immigration:
DP(value-iteration)->RL(QL) and RL(QL)->DQN?
Why the reason seems to be equivalent? What is the difference?
What is wrong in my clarification?
reinforcement-learning q-learning dqn
asked 20 mins ago
user10296606user10296606
1937
$endgroup$
add a comment |
$begingroup$
In the following of my previous question : Difference between DP and Q-learning?
When we immigrate from DP to MDP and when we immigrate from a MDP (Q-learning) to DQN?
I have some problem in the reason of immigration from DP to a MDP like Q-learning and from Q-learning to DQN? It seems to be so similar what is the difference of the reasons?
Please check the validity of my sentences or correct it:
If there are 5 variables in each time step which each of them could give one of 3 possible value and it is running for 100 time steps, Then making all pairs of states which is equivalent to the filling completely the Q-learning in running time steps is not possible ( #states=(5 ^ 3) ^ 100), then it is essential to migrate from QL to DQN.
=> We are able to say that the enormous states leads to immigrate from Q-learning to DQN? Is it right?
On contrast, DP is immigrated to RL when probability of observing the next state and reward based on the present state and action is not available. This reason seems to be the same and large enormous states leads to immigrate from DP to MDP?
What is the difference between these two types of the immigration:
DP(value-iteration)->RL(QL) and RL(QL)->DQN?
Why the reason seems to be equivalent? What is the difference?
What is wrong in my clarification?
reinforcement-learning q-learning dqn
asked 20 mins ago
user10296606user10296606
1937
$endgroup$
In the following of my previous question : Difference between DP and Q-learning?
When we immigrate from DP to MDP and when we immigrate from a MDP (Q-learning) to DQN?
I have some problem in the reason of immigration from DP to a MDP like Q-learning and from Q-learning to DQN? It seems to be so similar what is the difference of the reasons?
Please check the validity of my sentences or correct it:
If there are 5 variables in each time step which each of them could give one of 3 possible value and it is running for 100 time steps, Then making all pairs of states which is equivalent to the filling completely the Q-learning in running time steps is not possible ( #states=(5 ^ 3) ^ 100), then it is essential to migrate from QL to DQN.
=> We are able to say that the enormous states leads to immigrate from Q-learning to DQN? Is it right?
On contrast, DP is immigrated to RL when probability of observing the next state and reward based on the present state and action is not available. This reason seems to be the same and large enormous states leads to immigrate from DP to MDP?
What is the difference between these two types of the immigration:
DP(value-iteration)->RL(QL) and RL(QL)->DQN?
Why the reason seems to be equivalent? What is the difference?
What is wrong in my clarification?
reinforcement-learning q-learning dqn
reinforcement-learning q-learning dqn
asked 20 mins ago
user10296606user10296606
1937
asked 20 mins ago
user10296606user10296606
1937
asked 20 mins ago
user10296606user10296606
1937
asked 20 mins ago
user10296606user10296606
1937
asked 20 mins ago
user10296606user10296606
1937
1937
add a comment |
add a comment |
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