README asset

[jcwleo]

[jcwleo]

[jcwleo]

[jcwleo]

[kslazarev]

https://github.com/jcwleo/random-network-distillation-pytorch/blob/master/config.conf Is this config the last one to get similar results as on images above?

I see last pull request is about normalization, maybe UseNorm = True improve reward_per_epi or speed of convergence? And what about UseNoisyNet, when it could better to use?

[jcwleo]

@kslazarev Hi, I used that config. but only NumEnv is 128 and MaxStepPerEpisode is 4500. In paper, author did not announce Advantage Norm and Noisynet. so I disabled that config.

[kslazarev]

Result by config in master branch.

MontezumaRevengeNoFrameskip-v4

Right now, set NumEnv==128 and MaxStepPerEpisode==4500. I'll attach result when get 1200-2000 updates.

[kslazarev]

@jcwleo I see the difference in x-axis scale in reward_per_epi and reward_per_rollout plots. On your MontezumaRevengeNoFrameskip-v4 image they are 1.200k and 12.00k (10x scale). But on my temporary progress image they are 200 and 600 (3x scale). Maybe need to change additional option in config?

[kslazarev]

Or the x-axis scale (global_update and sample_episode) depends on player survival/experience so on later updates x-axis scale will be the same?

[jcwleo]

@kslazarev per_rollout and per_epi is not same scale. per_rollout means just one global update(enter agent.train_model()). but per_epi means Env’s one episode info that is one of parallel env. If one episode’s total step is 1024 and Num_step(rollout size) is 128, each scale of x-axis is 8 times different.

[kslazarev]

@jcwleo Yes, correct. I have another small questions about code. How could be appropriate to ask? Every question as new issue, or move forward to ask in this issue?

[jcwleo]

@kslazarev I want you to create an issue for each question. :)

[kslazarev]

NumEnv=128 and MaxStepPerEpisode==4500

Looks similar as in README. On NumEnv=128 I've stopped the process because swap is used.

[xiaioding]

Hello, can you tell me how many Gpus you used and how long it took you to see this effect?

[kslazarev]

Hello. Not fast. Don't remember exactly, 1 or 2 NV 1080 Ti

[xiaioding]

@kslazarev Excuse me, I use 1 3090,2 envs, run for more than 2 hours, the reward is still 0, is this normal? I didn't load the pre-training model

[kslazarev]

It was 3 years ago. Could not help, I don't remember exactly what problem could cause.

[xiaioding]

@kslazarev Ok, thanks

[jcwleo]

@kslazarev Thank you for answering for me.