I’m having trouble getting the OpenAI universe starter agent to train correctly. I set it up on an AWS m4.16xlarge instance with 32 workers, but after running it for more than 30 minutes, the agent shows no improvement at all. The documentation mentions that the agent should be able to solve the environment in just 10 minutes, so something is definitely wrong.
I’m using TensorBoard to track the progress. The GitHub example shows results with 16 workers reaching an episode reward of 21 in 30 minutes. Even though I doubled the worker count, my setup isn’t learning anything after the same time period. I checked the logs and there are no compilation errors.
One thing that seems suspicious is this “failed to connect to server” error message that keeps appearing during execution, though it doesn’t stop the program from running.
Has anyone successfully run this starter agent before? Did you encounter similar problems with the training not progressing? Any suggestions on what might be causing this issue would be really helpful.
Had the same issue with universe agents - it’s usually an environment setup problem. Those connection errors mean your VNC display servers aren’t starting up before the workers try to connect. You need X virtual framebuffer running with the right display config for universe to work. Also double-check your universe version. Newer versions had compatibility bugs that caused silent failures - agents looked like they were running but couldn’t actually see the game. Start with one worker to figure out if it’s a setup issue or scaling problem. If one worker trains fine, then you know it’s something with your multi-worker config.
Your worker count might be the problem. I hit the same issue when I scaled up too fast - more workers doesn’t always mean faster learning, especially if your instance can’t handle the load. The m4.16xlarge has decent CPU but universe environments are resource-heavy with 32 parallel instances running. Try dropping to 16 workers first and see if that fixes things. Check your memory usage too - I’ve seen systems start swapping to disk and performance just dies. Those connection errors could be workers fighting over VNC connections. Also verify your learning rate and hyperparameters are set right for more workers - some setups need tweaking when you scale up.
also check ur docker setup - universe needs containers running for each env. if they’re not spinning up right, u’ll get those connection failures. same thing happened to me - containers would start then crash because of memory limits or missing dependencies.
that server connection error is ur problem. universe agents can’t see the game environment without proper VNC connections - if it’s failing to connect, ur agent’s training blind. check if ur VNC servers are running and make sure AWS security groups aren’t killing them.
The Problem: You’re encountering training issues with the OpenAI Universe starter agent, specifically experiencing slow or no progress despite using an AWS m4.16xlarge instance with 32 workers. The agent isn’t learning, and you’re seeing persistent “failed to connect to server” errors in the logs. Your current setup is based on the GitHub example, but increasing the worker count from 16 to 32 hasn’t yielded improvement.
Understanding the “Why” (The Root Cause):
The core issue is likely related to the OpenAI Universe environment setup and its limitations, especially when scaling worker counts. The “failed to connect to server” errors strongly indicate problems with the VNC connections necessary for the agents to interact with the game environment. Universe is now largely considered deprecated technology, making robust scaling and debugging challenging. Simply adding more workers won’t guarantee faster training; if the environment setup is faulty, additional workers will exacerbate the connection issues and resource contention. The resource demands of running 32 parallel Universe environments might overwhelm your AWS instance, leading to performance degradation or instability.
TL;DR: The Quick Fix:
Consider migrating away from OpenAI Universe. The original poster suggests Latenode as a modern alternative.
Step-by-Step Guide (Alternative Solution): Migrating to Latenode
Evaluate Latenode: Explore Latenode’s features and documentation to assess its suitability for your reinforcement learning workflow. This involves understanding how it handles environment setup, scaling, and data management. This might entail creating a Latenode account and familiarizing yourself with their platform.
Replicate your environment: The exact steps will depend on the specifics of your current PongDeterministic-v3 setup. You’ll need to transfer or recreate the relevant code and environment configuration within the Latenode environment. This might require adapting existing scripts to work with Latenode’s API and infrastructure.
Build your workflow: Implement your training process using Latenode’s automated tools and features. This should involve configuring the training parameters (worker count, hyperparameters etc.) within the Latenode system. Latenode aims to handle the complexities of scaling and environment management, streamlining the workflow and mitigating VNC connection issues.
Monitor and scale: Track training progress within Latenode and adjust worker counts as needed based on performance metrics.
Common Pitfalls & What to Check Next:
Resource Limits: Even if you switch to Latenode, monitor CPU, memory, and network usage on your AWS instances. Ensure you have sufficient resources allocated to handle the training load.
Hyperparameters: Experiment with different hyperparameters, particularly learning rates, as these can significantly impact training performance, especially when scaling worker counts.
Alternative Environments: If you choose not to migrate, consider experimenting with simpler, more modern environments that don’t rely on OpenAI Universe’s VNC infrastructure.
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!
Understanding the “Why” (The Root Cause):
Step-by-Step Guide (Alternative Solution): Migrating to Latenode
Common Pitfalls & What to Check Next:
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!