Comments (4)
Why is there 6 actions in ‘PongNoFrameSkip-v4’ environment?
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Hi,
I'll try to address your questions in order:
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I'm not sure I understand this one. Both expected_q_values and current_q_values have dimensionality [batch_size x sequence_length], where expected_q_values[:, -1] and current_q_values[:, -1] would be the most recent timestep
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Good question, I didn't replicate the original paper exactly, which uses mean squared error. Instead, I used the huber loss function (https://en.wikipedia.org/wiki/Huber_loss).
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Another good question. Once again, I did not replicate the original paper exactly. Masking the first half of the losses is a trick introduced in (https://arxiv.org/abs/1609.05521). In summary, the hidden state used to calculate the first few q -values of the sequence is unlikely to be correct since the hidden state is initialized to zero for each sequence; empirically it improved performance.
from deeprl-tutorials.
To address your followup question, see https://github.com/openai/gym/wiki/Table-of-environments for more info on openai gym environments
from deeprl-tutorials.
Thank you for your help, I have get them.
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Related Issues (10)
- Quantile-Rainbow: is gamma discounting for n-step rewards included? HOT 1
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- Model has no finish_nstep HOT 3
- Flickering Pong POMDP HOT 13
- 01.DQN.ipynb all_rewards, losses not defined? HOT 4
- About code HOT 6
- DQN not learning on stacked frame inputs HOT 10
- Reward does not increase in Colab HOT 2
- Running error in 03.Double_DQN.ipynb HOT 3
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