Comments (2)
It highly depends on what the game is. If a game needs a long series of steps to make a decision, you need to increase history_length
. If the game's state is diverse, you may need to increase memory_size
. If the game is hard to learn, you need to increase max_steps
. If the input of the game has higher dimension, you need to change the structure of the network to represent the state more clearly.
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Thanks really a lot !
from dqn-tensorflow.
Related Issues (20)
- Trouble loading checkpoints HOT 1
- How to run for more epochs HOT 1
- History is not updated with new game screen created after a terminal state is reached HOT 2
- CUDA_ERROR_OUT_OF_MEMOR HOT 1
- KeyError: '__flags' HOT 4
- unsupported operand type(s) for +: 'dict_values' and 'list HOT 3
- error memory HOT 1
- No updates to the learning rate
- clipped error function is not correct
- why the content is not match the readerme?
- How to run on Windows? HOT 2
- what's the difference between simple game and detail game
- about starting a new game and History HOT 1
- terminal in agent.py seem not handle properly HOT 1
- Any one who can share model details? HOT 3
- AssertionError: Cannot call env.step() before calling reset() HOT 1
- load failed HOT 3
- How to play the code?
- why the Breakout-v0's action_size is 4 ,but in the checkpoint is 6 HOT 1
- Ram consumption HOT 1
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