kandi X-RAY | ml-agents Summary
kandi X-RAY | ml-agents Summary
Unity Machine Learning Agents Toolkit
Top functions reviewed by kandi - BETA
ml-agents Key Features
ml-agents Examples and Code Snippets
behaviors: BehaviorPPO: trainer_type: ppo hyperparameters: # Hyperparameters common to PPO and SAC batch_size: 1024 buffer_size: 10240 learning_rate: 3.0e-4 learning_rate_schedule: linear # PPO-specific
```sh # Install Xorg $ sudo apt-get update $ sudo apt-get install -y xserver-xorg mesa-utils $ sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024 # Get the BusID information $ nvidia-xconfig --query-gpu-info # Add the BusID inform
conda create -n trainer-env python=3.8.13 conda activate trainer-env def create_policy( self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec ) -> TorchPolicy: actor_cls: Union[Type[SimpleActor], Type[SharedActorCrit
import numpy as np import mlagents from mlagents_envs.environment import UnityEnvironment # ----------------- # This code is used to close an env that might not have been closed before try: unity_env.close() except: pass # -------
from unityagents import UnityEnvironment def main(): env = UnityEnvironment(file_name='FrozenLakeGym') state = env.reset(train_mode=True) result = env.step(0) print(result) env.close() if __name__ == "__main__": m
Trending Discussions on ml-agents
I'm following the tutorial in the documentation of ml-agents from release 18. I'm not able to run the training of the example available. When I try to run the code with anaconda running I get the error
Error loading native library grpc_csharp_ext x64 dll and the mlagent don't connect to the python code and the agent just runs the heuristic mode.
ANSWERAnswered 2021-Dec-08 at 12:17
This was happening because the path to my unity code had special charactes such as ç and other accentuations like ã and á. I saw in a forum that Germans have special characters and when using them in the file names in the path it gave the same error (see here).
I'm working with the Cooperative push block environment ( https://github.com/Unity-Technologi...nvironment-Examples.md#cooperative-push-block) (exported in order to use the Python API) using the latest stable version. The issue is that I'm not getting the reward (positives or negatives). It is always 0. If I export the Single push block environment, I receive the rewards correctly. Below you have the code I'm using from the collab example https://github.com/Unity-Technologies/ml-agents/blob/main/docs/Python-API.md...
ANSWERAnswered 2021-Oct-06 at 07:04
I have received this answer from the Unity ml-agents GitHub issues section:
The DecisionStep also has a group_reward field which is separate from the reward field. The group rewards given to the Cooperative Pushblock agents should be here. We apologize that the collab doesn't point this out explicitly and I will make an update to it.
python version as...
ANSWERAnswered 2020-Jun-18 at 19:41
Like 'derHugo' already mentioned it is basically a duplicate.
You're pointing to the documentation of version 0.15 but using version 0.16.1
env.get_agent_groups() was replaced by
This is the documentation that matches your version
I'm using Unity3D ML-Agents and when running examples of multiple clones inside(3DBall for example), there is a message in the console says:
Couldn't connect to trainer on port 5004 using API version API-13. Will perform inference instead. UnityEngine.Debug:Log(Object) MLAgents.Academy:InitializeEnvironment() (at Assets/ML-Agents/Scripts/Academy.cs:228) MLAgents.Academy:LazyInitialization() (at Assets/ML-Agents/Scripts/Academy.cs:147) MLAgents.Agent:OnEnable() (at Assets/ML-Agents/Scripts/Agent.cs:255)
I tried to turn off the firewall but it didn't work. How can I solve it?
The version I'm using is...
ANSWERAnswered 2020-Jun-18 at 19:15
This is just a normal warning that tells you that you won't train but instead use an already trained version in the environment(s). You don't need to worry about this. I assume your environment works when you start it.
If you really want to turn this off, you can go to the agent object and look for the 'Behavior Parameters' -> 'Behavior Type' then set this value to "Inference". Make sure to set it back to default when you want to train your agents.
If you want a good introduction to MLAgents, make sure to check out my
YouTube ML-Agents Playlist
Edit: I just saw that you're using a beta version. Make sure to use at least version 0.16.0. Probably just going through my first video would be the best idea to get you started.
I've been using ML-Agents for several months now and have been working on a self-balancing pair of legs. Though, I've had a question that's been itching me since the day I've started: How do I KNOW for a fact that the agents are working together? All I've done is copy and paste the area prefab 9 times. Is that all you have to do to make the agents learn more efficiently? Or is there something else I'm missing? Thanks.
Agent Script >>> (I've not really needed to use any other scripts besides this one. Area and academy have nothing in them.)...
ANSWERAnswered 2020-Feb-10 at 03:10
I believe yes all you need to do is have multiple instances of the prefab. As long as there are multiple
Areas in the scene, they should be able to coordinate their batches for learning.
If you want to measure how having multiple areas changes things, I would have one area and let it play for some time, and look at a graph of cumulative reward vs. episode number and see how high it gets, then do the same thing with many areas and see how the same graph looks with that.
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