INPUT and OUTPUT of DeepMind paper to solve the ATARI game
$begingroup$
-INPUT and OUTPUT OF ATARI DQN:
In the abstract paragraph of the DQN work by DeepMind enter link description here 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 refer to Q-learning or DNN?
• What is said it learn control policy however DQN is composed of QL which is a value iteration algorithm not a policy iteration?
• variant type of Q-learning is vague? it is variant because approximation of Q-learning is implemented?
deep-learning reinforcement-learning q-learning dqn deep-network
asked 5 mins ago
user10296606user10296606
1957
$endgroup$
add a comment |
$begingroup$
-INPUT and OUTPUT OF ATARI DQN:
In the abstract paragraph of the DQN work by DeepMind enter link description here 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 refer to Q-learning or DNN?
• What is said it learn control policy however DQN is composed of QL which is a value iteration algorithm not a policy iteration?
• variant type of Q-learning is vague? it is variant because approximation of Q-learning is implemented?
deep-learning reinforcement-learning q-learning dqn deep-network
asked 5 mins ago
user10296606user10296606
1957
$endgroup$
add a comment |
$begingroup$
-INPUT and OUTPUT OF ATARI DQN:
In the abstract paragraph of the DQN work by DeepMind enter link description here 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 refer to Q-learning or DNN?
• What is said it learn control policy however DQN is composed of QL which is a value iteration algorithm not a policy iteration?
• variant type of Q-learning is vague? it is variant because approximation of Q-learning is implemented?
deep-learning reinforcement-learning q-learning dqn deep-network
asked 5 mins ago
user10296606user10296606
1957
$endgroup$
-INPUT and OUTPUT OF ATARI DQN:
In the abstract paragraph of the DQN work by DeepMind enter link description here 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 refer to Q-learning or DNN?
• What is said it learn control policy however DQN is composed of QL which is a value iteration algorithm not a policy iteration?
• variant type of Q-learning is vague? it is variant because approximation of Q-learning is implemented?
deep-learning reinforcement-learning q-learning dqn deep-network
deep-learning reinforcement-learning q-learning dqn deep-network
asked 5 mins ago
user10296606user10296606
1957
asked 5 mins ago
user10296606user10296606
1957
asked 5 mins ago
user10296606user10296606
1957
asked 5 mins ago
user10296606user10296606
1957
asked 5 mins ago
user10296606user10296606
1957
1957
add a comment |
add a comment |
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