Comments (3)
你可以试试电脑每个核跑一个智能体
from deeprl_signal_control.
In my computer, I run the experiment in 1600 episode and finished in four days. It spend a lot of time for me to collect data.
So, I wonder if there is any way to speed up the experiment. And is it useful to change hyperemeters like episode_length_sec and control_interval_sec?
Hello. Did you fix it?
It took me a lot of time.
I only trained the model. and I had errors when evaluated. I did not know how to fix it
from deeprl_signal_control.
How about using "import libsumo as traci" instead of "import traci"?
It seems like a way worth trying.
see :
https://sumo.dlr.de/docs/Libsumo.html#using_libsumo
eclipse-sumo/sumo#11770
from deeprl_signal_control.
Related Issues (20)
- Adding phases. HOT 4
- Giving a minimum green duration. HOT 3
- About distance_map HOT 1
- How many steps does the neural network's lstm look past? HOT 6
- Can I use multi-threading during training? HOT 4
- Comparison between models and SUMO default traffic lights program HOT 4
- About modifing ma2c model. HOT 1
- Are there all evaluation data in one .xml file? HOT 2
- Configuration for small grid
- About A3c
- traci.exceptions.FatalTraCIError: connection closed by SUMO HOT 1
- Running DeepRL code issues HOT 3
- Is there a PyTorch version
- How to run the code
- Is there using global_reward for 'a2c', 'greedy' and independent rl? While the 'ma2c' is using the independent reward for training?
- Evaluate
- base-dir对应什么文件,在那个地方?
- Why choose the probability of the first four phases of a neighbor?
- IA2C Issue in the large grid
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from deeprl_signal_control.