Comments (6)
My model got 6100 when it run 16k rollout. :)
from random-network-distillation-pytorch.
omg....i broke out 2500, but already 1500 episode, still stay at 2900
from random-network-distillation-pytorch.
how many env did you use? I used 128 env
from random-network-distillation-pytorch.
I also used 128...TOT....
from random-network-distillation-pytorch.
It was also mentioned in the paper that performance was not very good. Why don't you learn more? Reinforcement learning is always a battle of patience. :)
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lol.....everyday morning, I just turn on the computer with great anticipation
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Related Issues (20)
- Reward converge at 4600 HOT 1
- Input shape is not correct in Linear-8 layer in CnnActorCriticNetwork feature model HOT 1
- Use several GPUs if they exist HOT 1
- Line 68 in train.py HOT 1
- global_grad_norm_ has no effect HOT 2
- Mario eval is slow HOT 2
- Generalized Advantage Estimator problem HOT 2
- what is meaning of line in envs.py: HOT 2
- I tried to train system but get error HOT 1
- input_size in model.py? HOT 1
- About sticky action HOT 1
- training error HOT 1
- if i want to employe this work to a new env, what should i do?
- Intrinsic reward calculation, sum or mean? HOT 2
- Action values are incremented by 1 for the Breakout game ?
- Extrinsic reward clipping
- README asset HOT 18
- refactoring make_train_data
- RNN λͺ¨λΈ μΆκ°
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