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Results tagged with dqn
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user 9793
For questions related to the deep Q-network (DQN), which is a deep neural network (e.g. a convolutional neural network) trained with a variant of Q-learning. The expression was coined in the paper "Playing Atari with Deep Reinforcement Learning" (2013) by Google's DeepMind.
3
votes
1
answer
7k
views
In DQN, updating target network every N steps or slowly update every step is better?
The use of target network is to reduce the chance of value divergence which could happen with off-policy samples trained with semi-gradient objectives. In Deep Q network, semi-gradient TD is used and …
- 520
7
votes
2
answers
778
views
Why don't people use projected Bellman error with deep neural networks?
DQN)? Instead, a less theoretical justified target network is used. …
- 520
3
votes
Why don't people use projected Bellman error with deep neural networks?
I have found some clues in Maei's thesis (2011): “Gradient Temporal-Difference Learning Algorithms.”
According to the thesis:
GTD2 is a method that minimizes the projected Bellman error (MSPBE).
G …
- 520