Comments (2)
Yap i thought in similar way but turned that the code seems work correctly.
- Reshaping issue
Here batch_size, trace_length are set to 4,8. Each Qnetwork object(main, target) receives batchtrace=32 frames. After conv4, dimension are turned into (32, 1, 1, 512) = (batchtrace, w, h, hidden units). - Non-zero H0 is iteratively updated and given to feed_dict[network.state]. This state is 'last hidden state' returned by each LSTM forward passing.
from deeprl-agents.
I had another thought. Isn't it unnecessary to have a target network for this notebook in the first place? Since you are setting the target network to be equal to the mainDQN right before training?
from deeprl-agents.
Related Issues (20)
- _ HOT 1
- simple and odd python problem HOT 2
- Double-Dueling-DQN: question about the rate to update target network
- Double-Dueling-DQN stops learning
- Can't see the source code. HOT 2
- checkGoal() in gridworld.py
- apply_gradients need a lock?
- A3C-Doom, is threading can make real parallelism?
- Please add more comments..
- Target network updates / Double-Dueling-DQN.ipynb HOT 1
- A3C Doom : function error
- DRQN plays FlappyBird
- what is the mean of multiply (1./(i+1))?
- Reward Smoothing
- A3C Doom: Why there should be no more workers than there are threads on CPU?
- How to do twice training session for the same buffer
- scipy.misc.imresize is deprecated in Scipy 1.14.3 --> modified code HOT 1
- A garbage code in Model-Network.ipynb
- Issue in DRQN
- Crash and burn in TF 2.0 and alter
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from deeprl-agents.