use python *.py to start A3C trainning, i get CreateSession still waiting for response from worker warning, and the master node never response.

[kismuz]

use python *.py to start A3C trainning, i get CreateSession still waiting for response from worker warning, and the master node never response.

[vincetom1980]

：）, the problem is stake=5000,when i change this parameter it works.

[Kismuz]

@vincetom1980,

the training episode is ok, but I always get 0 reward, do you know why?

no, but you definitely have to change broker account settings to match your trading instrument, cause those in example were set to match exact currency pair:

# Set leveraged account:
MyCerebro.broker.setcash(2000)
MyCerebro.broker.setcommission(commission=0.0001, leverage=10.0) # commisssion to imitate spread
MyCerebro.addsizer(bt.sizers.SizerFix, stake=5000,)  

Refer backtrader documentation for details.
Also, amplifier constant inside strategy class is instrument sensitive and may need tuning:

...
...
   def get_market_state(self):
        T = 2e3  # EURUSD
        # T = 1e2 # EURUSD, Z-norm
        # T = 1 # BTCUSD
...
...

make and attach here screenshot of images tab in tensorboard, including episode rendering and state input rendering. It can give some hints on what's going wrong.

[Kismuz]

@vincetom1980, no, I haven't seen this before. Seems to be distributed TF error. Have you tried to run it as notebook under jupyter kernel?

[vincetom1980]

yes，when �I use jupyter kernel the error disapeared. But it's really difficult for me to debug with jupyter for I don't know how to trace into the backend code.

Now I try to replace the input file DAT_ASCII_EURUSD_M1_201703.csv with the Future contract file, whose content like:
20171130 105900;2991.0;2993.0;2991.0;2991.0;776.0
20171130 110000;2992.0;2997.0;2995.0;2991.0;2004.0
20171130 110100;2995.0;2995.0;2993.0;2992.0;666.0
20171130 110200;2993.0;2996.0;2996.0;2993.0;644.0
20171130 110300;2996.0;2996.0;2995.0;2994.0;824.0
20171130 110400;2995.0;2995.0;2995.0;2994.0;324.0
20171130 110500;2994.0;2998.0;2997.0;2994.0;1546.0
20171130 110600;2998.0;3000.0;2999.0;2997.0;2078.0
20171130 110700;3000.0;3000.0;2996.0;2995.0;1166.0
20171130 110800;2997.0;2997.0;2994.0;2993.0;1260.0
20171130 110900;2993.0;2996.0;2995.0;2993.0;768.0
20171130 111000;2995.0;2996.0;2995.0;2994.0;606.0
20171130 111100;2994.0;2995.0;2992.0;2991.0;1948.0
20171130 111200;2992.0;2992.0;2988.0;2988.0;3150.0
20171130 111300;2989.0;2990.0;2989.0;2987.0;1932.0
20171130 111400;2990.0;2992.0;2992.0;2989.0;1060.0
20171130 111500;2992.0;2993.0;2993.0;2991.0;626.0
20171130 111600;2993.0;2994.0;2992.0;2992.0;720.0
20171130 111700;2992.0;2993.0;2989.0;2988.0;1266.0
20171130 111800;2989.0;2990.0;2987.0;2985.0;1718.0
20171130 111900;2987.0;2991.0;2990.0;2986.0;2018.0
20171130 112000;2991.0;2993.0;2991.0;2990.0;1768.0
20171130 112100;2991.0;2991.0;2984.0;2983.0;3684.0
20171130 112200;2984.0;2989.0;2987.0;2983.0;5100.0
20171130 112300;2988.0;2988.0;2986.0;2984.0;2464.0

the training episode is ok, but I always get 0 reward, do you know why? I have to stop here for nearly 3 days, than you for your help.