Open darwinharianto opened 2 years ago
I have the same question. Can someone tell me how to visualize the experimental environment? Thank you very much.
I have the same question. Can someone tell me how to visualize the experimental environment? Thank you very much.
From README:
Learnt models can be loaded using the
checkpoint_path
parameter, after which the learning will proceed from the corresponding timestep.
So I guess something like this:
python3 src/main.py --config=mappo --env-config=gymma with env_args.key="rware-1color-tiny-4ag-v1" checkpoint_path="/PATH_TO_MODEL"
You probably need to wrap the env around a render wrapper too, and make batch_run_size=1
so that you don't get multiple windows at a time.
Yeah, same question from me - I posted a similar question a few days ago before I saw this. It looks like rendering is not currently implemented for non-sc2 environments. From my search it looks like the save_reply
method needs to be manually implemented or a render
flag will need to be added to the config and within the runner code. Hoping I'm wrong though and there's an out-of-the-box solution that I'm just missing
I'm trying to run MAPPO or IPPO with sc2 environment but I couldn't figured out how to specify the maps. I already us pymarl and run the algorithms with this code:
python3 src/main.py --config=qmix --env-config=sc2 with env_args.map_name=2s3z
I want to run epymarl with MAPPO or IPPO algorithms with the same map 2s3z from sc2. could any one help me with that? I'm using this code:
python3.7 src/main.py --config=mappo --env-config=sc2 with env_args.time_limit=25 env_args.key="SMAC:2s3z"
I get this error:
sacred.utils.ConfigAddedError: Added new config entry that is not used anywhere Conflicting configuration values: env_args.key=SMAC:2s3z
python src/main.py --config=qmix --env-config=gymma with env_args.time_limit=25 env_args.key="mpe:SimpleSpeakerListener-v0" checkpoint_path="your path" evaluate=True render=True
Thank you for sharing!!
Using colored RWARE environment, I want to ask how to load and visualize trained models on colored rware