nullspace-colombia / unray-bridge Goto Github PK

Hi,
I want to create RL Based Driving Car System, i used Cartpole BP and set everything but i think something was missed in my Implementation,
how to define minus reward or using trace channel to Observation system??!!
I have RL based car and Goal actor in my scene, i used distance between car and goal actor and connected value to Observation function.




Thank You for your help
Best Regards.

Hey Team,

I'm running into issues trying out both examples you have provided.

Issue

When running in command prompt python parallel_multiagentArena.py
I receive this error:
OverflowError: int too big to convert
And when running in command prompt python main_cartpole.py
I receive this error
[ CONNECTION ] connecting to localhost port 10010
Trying to connect...
Trying to connect...
Trying to connect...
Trying to connect...
Connection Timeout!

What's interesting is when running in command prompt netstat -a | findstr :10011 I do get a response from UE5.3
TCP 0.0.0.0:10011 www:0 LISTENING

Steps to reproduce

Create a new VirtualEnv with Anaconda 3.

1. Use Python 3.10.x (have tried 3.8.x as well)
2. git clone https://github.com/Nullspace-Colombia/unray-bridge.git
cd unray-bridge
pip install -r requirements.txt
pip install tensorflow
3. In Anaconda 3 select your new Virtual Environment and click the green play button and select Open Terminal
4. Enable the UE 5.3 plugin Remote Control API
5. Restart UE 5.3
6. Inside Project Settings, select Plugins - Remote Control
7. Under Remote Control WebSocket Server Port, enter in 10011
8. Select map MultiAgentArena
9. Press the Green Play button in the UE 5.3 Editor
10. In your Terminal (command prompt)
11. Run in command prompt python main_multiagentArena.py
12. Wait for error

Hardware

• Intel Core i9 12900KF
• ASUS TUF GAMING Z690-PLUS WIFI D4
• Corsair 32 GB DDR4 @3200mhz
• Samsung SSD 980 PRO 2TB
• NVIDIA GeForce RTX 3090

Suggestions for clearer instruction for users exploring Unray examples

Cartpole Example:

1. Change the port in Connector_SA from 9443 to 10010 to match main_cartpole.py requirements

Before:

After:

Multiagent Example

1. Change the port in Connector_MA from 9443 to 10010
2. Change the port in MultiAgent_Env from 9443 to 10010
3. Change the port in MultiAgentArena from 9443|0 to 10011 to match main_multiagentArena.py requirements. Ensure the second Base Port under Class Defaults is 10010 which is pulled from MultiAgent_Env

Before:

After:

Before:

After:

Before:

After: