Miffyli 333
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MineRL Labs - Carnegie Mellon University
Sample-efficient reinforcement learning in Minecraft
Latest submissions
See All| failed | 25431 | ||
| graded | 25415 | ||
| graded | 25316 |
Unity Technologies
A new benchmark for Artificial Intelligence (AI) research in Reinforcement Learning
Latest submissions
See All| graded | 8597 | ||
| graded | 8541 | ||
| graded | 8531 |
NeurIPS 2019 : MineRL Competition
When will the results of round 1 be announced?
5 months agoI do not have an answer for that, but organizers are posting clips of top agents on Discord, most recent from roughly 6h ago. Linking them here so non-Discordians can also see them:
Partially rendered env in MineRLObtainDiamondDense-v0
5 months agoMy wild guess is this has something to do with rendering of chunks and/or world-generator generating chunks. Similar things happen if you travel too fast to one direction (you reach “edge of the world”, but it will appear eventually). Not sure why previously rendered chunks would disappear like that.
Equip item failed
6 months agoSorry for double-posting, but just for clarity:
Tested on Ubuntu 18.04 (Python 3.6) and Windows 10 (Python 3.7) with MineRL 0.2.4:
- Create ObtainDiamond-v0 environment, control by manually creating actions
- Open chat (press “T”) and give some items
/give @a wooden_pickaxe 1and/give @a stone_pickaxe 1. - Wooden pickaxe is equiped (auto-equip the first slot).
- Using
equip="stone_pickaxe"orequip="air"does not work, nor do the integer versions (3 and 1, respectively)
How to use furnace to "nearbySmelt" coal?
6 months agoTested this on Windows 10 one more time, still no luck :(.
Random thought: Perhaps it works but it creates charcoal, and since charcoal != coal it won’t show up in the inventory list? They are very distinct items in Minecraft, but I am not sure if Malmo or your code abstracts this one out.
How to use furnace to "nearbySmelt" coal?
6 months agoI tested it like this (Ubuntu 18.04, Python 3.6, MineRL 0.2.4):
- Create env and control agent by feeding user input
- Focus on game, press “T” and command yourself necessary items (
/give @a furnace 1,\give @a log 64and/give @a planks 64) - Place furnace, face it and try to use
nearbySmelt=2 (="coal"). Nothing should happen (no crafting/smelting).
Repeated reward for logs and furnace
6 months agoTried this as well with no luck 
(tried “wooden_pickaxe”, “none”, “air” and “stone_pickaxe”)
How to use furnace to "nearbySmelt" coal?
6 months agoAh my bad. Indeed you should be able to burn logs into charcoal in default Minecraft but indeed the action nearbySmelt[coal] does not do anything after quick testing. It is either bugged or refers to something else (remnant of traditional smelting action?).
This has also been discussed here: How is the "reward" on leaderboard page computed? (Edit: Oh, it was you who reported this ^^’)
Equip item failed
6 months ago+1 on the equip command not working (can not unequip items nor equip anything). In fact it did not work in version 0.2.3 for us either.
How to use furnace to "nearbySmelt" coal?
6 months agoMineRL uses Malmo’s functions to make such smelting easier, essentially turning it into crafting. You only need to have iron ore and coal in your inventory, and once you do nearbySmelt iron you should get an iron ingot immediately. This was discussed on some other thread here on same channels.
Block become much small when looking from top
7 months agoThat depends on the field-of-view of the player. You seem to be playing with the default 70 FOV (the lower picture), where as the recordings seem to be done in maximum 110 FOV (upper picture). This is based on quick round in vanilla Minecraft.
Main hand observations
8 months agoI believe maxDamage refers to how damaged the tool can get. I.e. If damage = maxDamage, then tool is broken and can not be used (as per Minecraft mechanics).
As for the none and air type: I am puzzled by this as well. Does one of them of never happen (they mean the same thing), or does none mean the player is not able to hit anything (“doesn’t even have hands”)?
Unity Obstacle Tower Challenge
Different results between training, debug and evaluation
9 months agoCould it be you overfit to the 100 seeds we have on shared binary of OT environment? AFAIK the evaluation uses seeds outside these 100.
We had something similar: Training model further did not make results more reliable in evaluation.
Why I was blocked from uploading new models for 3 days
9 months agoI use Linux (Ubuntu 16.04), and I dunno it works for other platforms.
Why I was blocked from uploading new models for 3 days
9 months agoTry running the environment without docker with same environment variables and port argument as in the examples. This works for me when I test my submission image.
Is the evaluation seed truly random?
9 months agoAs I have understood it, the seeds are fixed (e.g. here Juliani tests one of the seeds). The wording on main page about random five seeds probably refers to five randomly preselected seeds.
UnityTimeOutException in evaluation
9 months agoWe only updated one of the model files after a successful submission. Worker ID is set to zero and environment filename to None.
Looks like that submission (29) did eventually manage to run through. Did you happen to re-queue the submission or did it work out on itself? Edit: Looks like you re-queued it.
Edit2: Looks like that submission (29) is now stuck and does not finish correctly ^^’
Edit3: Looks like we figured it out: When testing locally outside docker images, the OT game ended up stuck with empty screen (skybox) and our code also waited for the OT environment. We first had to launch our agent code and wait until it tries to connect to OT environment (“Start training by pressing the Play…”), after which we launched OT env and the evaluation started successfully. The solution for submission was to modify agent code to first create OT environment before everything else, including larger imports (TF, Torch, etc), and then proceed with creating/loading agents.
UnityTimeOutException in evaluation
9 months ago@mohanty: That’s odd o: . I have another debug run going on right now, and it seems to be stuck. So far 500 seconds waited and no luck: https://gitlab.aicrowd.com/Miffyli/obstacletower-2019/issues/29.
But I will keep trying every now and then, if it would eventually work out. Thanks for the help!
UnityTimeOutException in evaluation
9 months agoLooks like I am running into same issue as described above: Previously submitted code suddenly is not working, and debugging gives me the timeout error. timeout_wait=300 did not solve the issue, but I will try with longer values. Here is one of the failed submissions: https://gitlab.aicrowd.com/Miffyli/obstacletower-2019/issues/25 .
Edit: timeout_wait=900 did not help either, still getting same error :/. https://gitlab.aicrowd.com/Miffyli/obstacletower-2019/issues/26
Submitting in debug mode sets leaderboard score to 0
9 months agoYes. Also note that debug runs are done with different seeds than final evaluation.
More information here: Announcement: Debug your submissions
Architectures of Round 1 winners
10 months agoApart from using improved Dopamine Rainbow, here is a short list of nuggets of information other people have shared:
- unixpickle’s blog post on hierarchical RL: https://blog.aqnichol.com/2019/04/03/prierarchy-implicit-hierarchies/
- Ross Wightman’s different version of Rainbow (got better results than the Dopamine one): https://github.com/rwightman/obstacle-tower-pytorch-rainbow
- Joe Booth got level 10 with “pure model-free RL” on standard-ish consumer hardware: https://twitter.com/iAmVidyaGamer/status/1122325568949633024
Vector Observation contents
10 months agoLooking at how ObstacleTowerEnv handles this observation, the vector tells number of keys as one-hot encoding (or with 1s up till number of keys, e.g. two keys would be [1,1,1,0,0,0]). So [1,0,0,0,0,0] means zero keys.
Vector Observation contents
10 months agoApologies if this sounds bit naive, but make sure you are importing the correct Python libraries. E.g. do you have obstacle_tower_env.py file in the directory from which you run the experiments?
You can find the imported file with the following:
In [1]: import obstacle_tower_env
In [2]: obstacle_tower_env.__file__
Out[2]: '/home/USERNAME/.local/lib/python3.5/site-packages/obstacle_tower_env.py'
V2.0 performance drop
11 months agoIf it is any help, we do the reset like this:
config = {"total-floors": 25}
env.reset(config)
Granted, we have not tried changing configs mid-run.
On the topic of configs: Some of the variables did not seem to have an effect of game (visual-theme and allowed-rooms), but this is only based on very brief experimentation so might be just a brainfart in our end.
V2.0 performance drop
11 months agoI haven’t measured FPS of the v2.0 env yet, but noticed two things:
- v2.0 is prettier: It has glows and fancier key models/textures. Perhaps this affects the performance little bit?
-
reset()takes much longer now with default config. Settotal-floorsreset parameter to 25 or lower to speed up reset considerably.
Vector Observation contents
11 months agoJudging by this function in obstacle_tower_env, the first six elements are for the number of keys (Perhaps one-hot? Not sure). Seventh is the time remaining and eight is the floor number, as in v1.3.
Question about the “Round 1 of Unity’s Obstacle Tower Challenge” email
11 months agoTax stuff: Ah yes, sounds reasonable. Would be bit of a mess if one of the Round 2 top3 would not be able to get the prize (assuming they want it).
On 4b): All of our team members received the email asking for ID and signature on the rules, and team lead got email with different greeting (“Dear Team Leader…”). I imagine they need everyone to sign the rules.
Question about the “Round 1 of Unity’s Obstacle Tower Challenge” email
11 months agoI second the post by @ipv6 , especially the parts (1,3) and (4):
(1,3) GCP credit might be tax-able in our country, so indeed it’d be nice to move to Round 2 without the GCP credits (and without the tax form).
(4) We in our do not have address in our standard IDs, although with more time we might be able to get such IDs.
About the rule on pre-trained model
5 months agoI would assume no pretrained models are allowed, and this is enforced by removing “large” files.
If so, it is too relaxed. Our models are well below 10MB, regardless of the network architecture, and even if models were larger you could prune them to 90% of the original size and still retain most of the performance.