Comments (3)
Managed to solve this by introducing an array into the preprocessFrame wrapper into itself for evaluation purposes! But an official approach would be welcome!
from deep-q-learning-paper-to-code.
Check out lecture #39. I show how to save video of the agent playing the game, and it renders with the original frames (not our grayscale / downsized images).
from deep-q-learning-paper-to-code.
Thank you Phil,
You'll find my adaptation for your code for Doom here
https://github.com/EXJUSTICE/Doom_DQN_GC/tree/master/PytorchDQN
Will you be doing a course on policy gradient models? Or is the Actor Critic course sufficient to cover the area in your opinion?
Also, I notice that that in DDQN, you specify that we must calculate S (t+1) through the eval and target networks, in this case S (t+1) is a state action value, correct? , as you do end up calling T.argmax() on it later.
from deep-q-learning-paper-to-code.
Related Issues (14)
- Runtime error in cartpole_naive_dqn.py HOT 1
- Indention of DQN/preprocess_pseudocode is broken
- ndarray error
- No registered env with id: PongNoFrameskip-v4 HOT 2
- Network is not learning when convolutional layers are applied. HOT 2
- what if i set load_check point to true from argparser
- No gradient required for q_target calculation
- DQN implementation error
- The dqn_agent.py loss function is the wrong way around HOT 1
- IndexError: tensors used as indices must be long, byte or bool tensors HOT 5
- Warning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead.
- Regarding target calculations in DuelDDQN and indices
- Detach target nexwork in DQN
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from deep-q-learning-paper-to-code.