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karolisram 169

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Challenges Entered

Measure sample efficiency and generalization in reinforcement learning using procedurally generated environments

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graded 93478
graded 93477
graded 93390

Sample-efficient reinforcement learning in Minecraft

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graded 105464
failed 85789
graded 85592

Sample-efficient reinforcement learning in Minecraft

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graded 25413
graded 25412
graded 25075

A new benchmark for Artificial Intelligence (AI) research in Reinforcement Learning

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graded 8563
graded 8534
failed 8533
Gold 1
EulerLearner
May 16, 2020
Silver 0
Bronze 2
Trustable
May 16, 2020
Newtonian
May 16, 2020

Badges


  • May 16, 2020

  • May 16, 2020

  • May 16, 2020
  • Has filled their profile page
    May 16, 2020

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  • May 16, 2020

  • May 16, 2020

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  • May 16, 2020

  • May 16, 2020
  • Kudos! You've won a bronze badge in this challenge. Keep up the great work!
    Challenge: NeurIPS 2019 : MineRL Competition
    May 16, 2020
  • Great work! You're one of the top participants in this challenge. Here's a gold badge to celebrate the acheivement.
    Challenge: Unity Obstacle Tower Challenge
    May 16, 2020
Participant Rating
Participant Rating

NeurIPS 2020: Procgen Competition

Round 2 is open for submissions 🚀

2 months ago

Sounds good, thanks @shivam . Could you please also give us the normalization factors for the 4 private envs (Rmin, Rmax) ?

Round 2 is open for submissions 🚀

2 months ago

Will we be able to choose which submission to use for the final 16+4 evaluation? It might be the case that our best solution that was tested locally on 16 envs is not the same as the best one for the 6+4 envs on public LB.

Human score

2 months ago

So I was a little bored and decided to see how well I could play the procgen games myself.

Setup:

python -m procgen.interactive --distribution-mode easy --vision agent --env-name coinrun

First I tried each game for 5-10 episodes to figure out what the keys do, how the game works, etc.
Then I played each game 100 times and logged the rewards. Here are the results:

Environment Mean reward Mean normalized reward
bigfish 29.40 0.728
bossfight 10.15 0.772
caveflyer 11.69 0.964
chaser 11.23 0.859
climber 12.34 0.975
coinrun 9.80 0.960
dodgeball 18.36 0.963
fruitbot 25.15 0.786
heist 10.00 1.000
jumper 9.20 0.911
leaper 9.90 0.988
maze 10.00 1.000
miner 12.27 0.937
ninja 8.60 0.785
plunder 29.46 0.979
starpilot 33.15 0.498

The mean normalized score over all games was 0.882. It stayed relatively constant throughout the 100 episodes, i.e. I didn’t improve much while playing.

I’m not sure how useful this result would be as a “human benchmark” though - I could easily achieve ~1.000 score given enough time to think on each frame. Also, human visual reaction time is ~250ms, which at 15 fps would translate to us being at least 4 frames behind on our actions, which can be important for games like starpilot, chaser and some others.

How to save rollout video / render?

3 months ago

That worked, thank you!

How to save rollout video / render?

4 months ago

Does it work properly for everyone else? When I run it for 100 episodes it only saves episodes number 0, 1, 8, 27, 64.

Same marks on the testing video

5 months ago

It’s the paint_vel_info flag that you can find under env_config in the .yaml files. There are also some flags that are not in the .yaml files, but people are using (use_monochrome_assets, use_backgrounds). You can find all of them if you scroll down here: https://github.com/openai/procgen .
Should we actually be allowed to change the environment? Maybe these settings should be reset when doing evaluation?

NeurIPS 2020: MineRL Competition

Error using gym.make

4 months ago

Working Colab example (credit to @tviskaron):

!java -version
!sudo apt-get purge openjdk-*
!java -version
!sudo apt-get install openjdk-8-jdk

!pip3 install --upgrade minerl
!sudo apt-get install xvfb xserver-xephyr vnc4server
!sudo pip install pyvirtualdisplay

from pyvirtualdisplay import Display
display = Display(visible=0, size=(640, 480))
display.start()

import minerl
import gym
env = gym.make(‘MineRLNavigateDense-v0’)

obs = env.reset()
done = False
net_reward = 0

for _ in range(100):
action = env.action_space.noop()

action['camera'] = [0, 0.03*obs["compassAngle"]]
action['back'] = 0
action['forward'] = 1
action['jump'] = 1
action['attack'] = 1

obs, reward, done, info = env.step(
    action)

net_reward += reward
print("Total reward: ", net_reward)

env.close()

Unity Obstacle Tower Challenge

Submissions are stuck

Over 1 year ago

There was a mention about the final standings for round 2 being based on more seeds than 5 to get a proper average performance. Is that going to happen? I didn’t try to repeatedly submit similar models to overfit the 5 seeds for that reason.

Is there any due date of GCP credit?

Over 1 year ago

mine says it expires 28 May 2020, not sure if that’s a set date or depends on when you redeem. I can’t find the date of when I redeemed.

Successful submissions do not appear on the leaderboard

Over 1 year ago

Is the debug option off?

What reward receives the agent for collecting a key?

Over 1 year ago

0.1, same as a single door (there’s 2 doors in each doorway).

Announcement: Debug your submissions

Over 1 year ago

And I was thinking I’m going mad when my previously working submission suddenly broke after “disabling” debug :slight_smile:

Submission Failed: Evaluation Error

Over 1 year ago

Can’t wait! I’ve been trying to get my dopamine trained agent to be scored (only 5-7 floors so far), but the only response I get after every change is
The following containers terminated prematurely. : agent
and it’s not very helpful. It builds fine, but gets stuck on evaluation phase.

Human Performance

Almost 2 years ago

In the Obstacle Tower paper there is a section on human performance. 15 people tried it multiple times and the max floor was 22. Am I reading this right? I finished all 25 floors on my very first try without much trouble.
How far did everyone else get and how many runs did you do? We could try collecting more data and make a more accurate human benchmark this way.

Greetings!

Almost 2 years ago

thanks nub

Greetings!

Almost 2 years ago

:herb:

Greetings!

Almost 2 years ago

Greetings!

Almost 2 years ago

how curious?

Greetings!

Almost 2 years ago

duuuh

Greetings!

Almost 2 years ago

no. @discobot

Greetings!

Almost 2 years ago

:poop:

Greetings!

Almost 2 years ago

karolisram has not provided any information yet.