input and output of DQN
$begingroup$
I have two related questions first one is the inputs and outputs of my problem? whereas the second one is related to this concept written in the abstract paragraph of first work in DQN (2013 DeepMind) and the world variant Q-learning? what is refer to the variant: it is variant because approximation of Q-learning is implemented?
First part:-INPUT and OUTPUT OF MY PROBLEM:
When I received (D) in each time step, I find a feature (X) in the output layer of my DNN and this feature (X) is used to update (O) and then by using updated (O) and next (D), (A) and (B) are updated.The reward is related to A, B, and O
(reward=A + 100 B + 80 if O>0 or reward=A + 100 B if O<=0).
Please correct my answers if I am wrong:
- What are the inputs and outputs of DQN?
Answer: Input:D Output:? (Reward or the last layer of DL which is X? (My confusing section is related to sentences of abstract paragraph of DQN deepmind 2013)
- What about the input and output of QL section?
Answer: Input: state (D,O,A,B) Output: O (My confusing section is related to sentences of abstract paragraph of DQN deepmind 2013)
Action: O
reward=as defined
- What the input and output of DL section?
Answer: Input: state (D,O,A,B,action) output: X
Second part:-INPUT and OUTPUT OF ATARI and generally speaking of a DQN:
In the first work by DeepMind group it has written
"
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The
model is a convolutional neural network, trained with a variant of Q-learning,
whose input is raw pixels and whose output is a value function estimating future
rewards. "
Would you please explain the bold sections? Input and output of Q-learning is explained or DNN?
What it is said it learn control policy however DQN is composed of QL which is a value iteration algorithm not a policy iteration?
deep-learning q-learning dqn
asked 1 hour ago
user10296606user10296606
1937
$endgroup$
add a comment |
$begingroup$
I have two related questions first one is the inputs and outputs of my problem? whereas the second one is related to this concept written in the abstract paragraph of first work in DQN (2013 DeepMind) and the world variant Q-learning? what is refer to the variant: it is variant because approximation of Q-learning is implemented?
First part:-INPUT and OUTPUT OF MY PROBLEM:
When I received (D) in each time step, I find a feature (X) in the output layer of my DNN and this feature (X) is used to update (O) and then by using updated (O) and next (D), (A) and (B) are updated.The reward is related to A, B, and O
(reward=A + 100 B + 80 if O>0 or reward=A + 100 B if O<=0).
Please correct my answers if I am wrong:
- What are the inputs and outputs of DQN?
Answer: Input:D Output:? (Reward or the last layer of DL which is X? (My confusing section is related to sentences of abstract paragraph of DQN deepmind 2013)
- What about the input and output of QL section?
Answer: Input: state (D,O,A,B) Output: O (My confusing section is related to sentences of abstract paragraph of DQN deepmind 2013)
Action: O
reward=as defined
- What the input and output of DL section?
Answer: Input: state (D,O,A,B,action) output: X
Second part:-INPUT and OUTPUT OF ATARI and generally speaking of a DQN:
In the first work by DeepMind group it has written
"
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The
model is a convolutional neural network, trained with a variant of Q-learning,
whose input is raw pixels and whose output is a value function estimating future
rewards. "
Would you please explain the bold sections? Input and output of Q-learning is explained or DNN?
What it is said it learn control policy however DQN is composed of QL which is a value iteration algorithm not a policy iteration?
deep-learning q-learning dqn
asked 1 hour ago
user10296606user10296606
1937
$endgroup$
add a comment |
$begingroup$
I have two related questions first one is the inputs and outputs of my problem? whereas the second one is related to this concept written in the abstract paragraph of first work in DQN (2013 DeepMind) and the world variant Q-learning? what is refer to the variant: it is variant because approximation of Q-learning is implemented?
First part:-INPUT and OUTPUT OF MY PROBLEM:
When I received (D) in each time step, I find a feature (X) in the output layer of my DNN and this feature (X) is used to update (O) and then by using updated (O) and next (D), (A) and (B) are updated.The reward is related to A, B, and O
(reward=A + 100 B + 80 if O>0 or reward=A + 100 B if O<=0).
Please correct my answers if I am wrong:
- What are the inputs and outputs of DQN?
Answer: Input:D Output:? (Reward or the last layer of DL which is X? (My confusing section is related to sentences of abstract paragraph of DQN deepmind 2013)
- What about the input and output of QL section?
Answer: Input: state (D,O,A,B) Output: O (My confusing section is related to sentences of abstract paragraph of DQN deepmind 2013)
Action: O
reward=as defined
- What the input and output of DL section?
Answer: Input: state (D,O,A,B,action) output: X
Second part:-INPUT and OUTPUT OF ATARI and generally speaking of a DQN:
In the first work by DeepMind group it has written
"
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The
model is a convolutional neural network, trained with a variant of Q-learning,
whose input is raw pixels and whose output is a value function estimating future
rewards. "
Would you please explain the bold sections? Input and output of Q-learning is explained or DNN?
What it is said it learn control policy however DQN is composed of QL which is a value iteration algorithm not a policy iteration?
deep-learning q-learning dqn
asked 1 hour ago
user10296606user10296606
1937
$endgroup$
I have two related questions first one is the inputs and outputs of my problem? whereas the second one is related to this concept written in the abstract paragraph of first work in DQN (2013 DeepMind) and the world variant Q-learning? what is refer to the variant: it is variant because approximation of Q-learning is implemented?
First part:-INPUT and OUTPUT OF MY PROBLEM:
When I received (D) in each time step, I find a feature (X) in the output layer of my DNN and this feature (X) is used to update (O) and then by using updated (O) and next (D), (A) and (B) are updated.The reward is related to A, B, and O
(reward=A + 100 B + 80 if O>0 or reward=A + 100 B if O<=0).
Please correct my answers if I am wrong:
- What are the inputs and outputs of DQN?
Answer: Input:D Output:? (Reward or the last layer of DL which is X? (My confusing section is related to sentences of abstract paragraph of DQN deepmind 2013)
- What about the input and output of QL section?
Answer: Input: state (D,O,A,B) Output: O (My confusing section is related to sentences of abstract paragraph of DQN deepmind 2013)
Action: O
reward=as defined
- What the input and output of DL section?
Answer: Input: state (D,O,A,B,action) output: X
Second part:-INPUT and OUTPUT OF ATARI and generally speaking of a DQN:
In the first work by DeepMind group it has written
"
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The
model is a convolutional neural network, trained with a variant of Q-learning,
whose input is raw pixels and whose output is a value function estimating future
rewards. "
Would you please explain the bold sections? Input and output of Q-learning is explained or DNN?
What it is said it learn control policy however DQN is composed of QL which is a value iteration algorithm not a policy iteration?
deep-learning q-learning dqn
deep-learning q-learning dqn
asked 1 hour ago
user10296606user10296606
1937
asked 1 hour ago
user10296606user10296606
1937
edited 17 mins ago
user10296606
asked 1 hour ago
user10296606user10296606
1937
asked 1 hour ago
user10296606user10296606
1937
asked 1 hour ago
user10296606user10296606
1937
1937
add a comment |
add a comment |
0
active
oldest
votes
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "557"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f44355%2finput-and-output-of-dqn%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
0
active
oldest
votes
0
active
oldest
votes
active
oldest
votes
active
oldest
votes
Thanks for contributing an answer to Data Science Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f44355%2finput-and-output-of-dqn%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
