MasterScrat

Start of the competition

Hello @RomanChernenko, you didn’t waste any time

The competition will start in the next days, stay tuned!

Cheers,
Florian

View post 1

Publishing the Solutions

Hello @fabianpieroth,

A recording of the presentations from top participants at the AMLD conference has recently been released: https://www.youtube.com/watch?v=rGzXsOC7qXg

The winning submissions as well as exciting news about the future of this competition will be released this month!

Cheers,
Florian

View post 2

Problems running in docker

I want to run my training code on AWS so I can make sure everything runs fine from start to finish on a machine slower that the official one. I am using a p2.xlarge instance with the “Deep Learning AMI (Ubuntu 16.04)”.

I am trying to run the code from the repo competition_submission_starter_template, without adding my own code for now. When I run ./utility/docker_train_locally.sh, I am faced with this error:

2019-10-22 02:01:29 ip-172-30-0-174 minerl.env.malmo.instance.868e96[39] INFO Minecraft process ready
2019-10-22 02:01:29 ip-172-30-0-174 minerl.env.malmo[39] INFO Logging output of Minecraft to ./logs/mc_1.log
2019-10-22 02:01:29 ip-172-30-0-174 root[62] INFO Progress : 1
2019-10-22 02:01:29 ip-172-30-0-174 crowdai_api.events[62] DEBUG Registering crowdAI API Event : CROWDAI_EVENT_INFO register_progress {'event_type': 'minerl_challenge:register_progress', 'training_progress': 1} # with_oracle? : False
Traceback (most recent call last):
  File "run.py", line 13, in <module>
    train.main()
  File "/home/aicrowd/train.py", line 75, in main
    env.close()
  File "/srv/conda/envs/notebook/lib/python3.7/site-packages/gym/core.py", line 236, in close
    return self.env.close()
  File "/srv/conda/envs/notebook/lib/python3.7/site-packages/minerl/env/core.py", line 627, in close
    if self.instance and self.instance.running:
  File "/srv/conda/envs/notebook/lib/python3.7/site-packages/Pyro4/core.py", line 280, in __getattr__
    raise AttributeError("remote object '%s' has no exposed attribute or method '%s'" % (self._pyroUri, name))
AttributeError: remote object 'PYRO:obj_3ec8abe8c48c4b4e9dd7f7b1ac4706b1@localhost:33872' has no exposed attribute or method 'running'
Exception ignored in: <function Proxy.__del__ at 0x7f4585d4f158>
Traceback (most recent call last):
  File "/srv/conda/envs/notebook/lib/python3.7/site-packages/Pyro4/core.py", line 266, in __del__
  File "/srv/conda/envs/notebook/lib/python3.7/site-packages/Pyro4/core.py", line 400, in _pyroRelease
  File "/srv/conda/envs/notebook/lib/python3.7/logging/__init__.py", line 1370, in debug
  File "/srv/conda/envs/notebook/lib/python3.7/logging/__init__.py", line 1626, in isEnabledFor
TypeError: 'NoneType' object is not callable
2019-10-22 02:01:30 ip-172-30-0-174 minerl.env.malmo.instance.868e96[39] DEBUG [02:01:30] [EnvServerSocketHandler/INFO]: Java has been asked to exit (code 0) by net.minecraftforge.fml.common.FMLCommonHandler.exitJava(FMLCommonHandler.java:659).

Where can I find more details? if I run ./utility/docker_run.sh --no-build to check in the container, I see no trace of logs.

Also, how would the trained model be saved in this situation? Is the the train folder mounted as a volume so that the model would be persisted outside of the container?

Finally, the expression $(PWD) in the bash files throws error for me.

View post 0

Partially rendered env in MineRLObtainDiamondDense-v0

Just happened again, seems to be related with large bodies of water.

View post 0

Partially rendered env in MineRLObtainDiamondDense-v0

I’ve just witnessed my agent interacting in an environment which looked partially rendered, ie large pieces appeared as transparent:

This is in MineRLObtainDiamondDense-v0. I am using minerl==0.2.7.

mc_1.log output around these times:

[10:51:00] [Client thread/INFO]: [CHAT] §l804...
[10:51:00] [Client thread/INFO]: [CHAT] §l803...
[10:51:00] [Client thread/ERROR]: Null returned as 'hitResult', this shouldn't happen!
[10:51:00] [Client thread/INFO]: [CHAT] §l802...
[10:51:01] [Client thread/INFO]: [CHAT] §l801...
[10:51:01] [Client thread/INFO]: [CHAT] §l800...

I don’t see anything else suspicious in this log file. The following episodes seem to be running correctly.

View post 0

Can't train in MineRLObtainIronPickaxeDense-v0 since 0.2.7

Great, thanks for the swift fix!

View post 1

Can't train in MineRLObtainIronPickaxeDense-v0 since 0.2.7

I just updated to 0.2.7, when trying to train in MineRLObtainIronPickaxeDense-v0 I now get the following errors:

ERROR    - 2019-10-18 04:52:00,768 - [minerl.env.malmo.instance.2edcf5 log_to_file 535] [04:52:00] [EnvServerSocketHandler/INFO]: [STDOUT]: REPLYING WITH: MALMOERRORcvc-complex-type.3.2.2: Attribute 'avoidLoops' is not allowed to appear in element 'RewardForPossessingItem'.
ERROR    - 2019-10-18 04:52:01,867 - [minerl.env.malmo.instance.2edcf5 log_to_file 535] [04:52:01] [EnvServerSocketHandler/INFO]: [STDOUT]: REPLYING WITH: MALMOERRORcvc-complex-type.3.2.2: Attribute 'avoidLoops' is not allowed to appear in element 'RewardForPossessingItem'.
ERROR    - 2019-10-18 04:52:02,950 - [minerl.env.malmo.instance.2edcf5 log_to_file 535] [04:52:02] [EnvServerSocketHandler/INFO]: [STDOUT]: REPLYING WITH: MALMOERRORcvc-complex-type.3.2.2: Attribute 'avoidLoops' is not allowed to appear in element 'RewardForPossessingItem'.
...

This environment was working fine before, but I was using the package version from before the reward loop was fixed, so maybe this problem was already present since 0.2.5.

View post 0

Tutorial Deep Reinforcement Learning to try with PyTorch

Incremental PyTorch implementations of main algos:
RL-Adventure DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
RL-Adventure-2 actor critic / proximal policy optimization / acer / ddpg / twin dueling ddpg / soft actor critic / generative adversarial imitation learning / HER

Good implementations of A2C/PPO/ACKTR: https://github.com/ikostrikov/pytorch-a2c-ppo-acktr

BTW The repo for the Udacity course is open source: https://github.com/udacity/deep-reinforcement-learning

View post 2