I’ve been looking at the OpenAI Universe starter agent that comes with documentation showing examples for Atari Pong and Flashdrive environments. I want to know if I can modify the envs.py file or other components to make this agent work with different gym environments, especially custom ones I’ve created.
Is the starter agent built specifically for those particular games, or can it be adapted for other environments? If it’s possible to modify it, what requirements would my custom environment need to meet? I’m wondering if there are specific interfaces or methods that need to be implemented for compatibility.
I’ve used the Universe starter agent with several custom environments - it’s pretty flexible. Your environment just needs to return observations in the right format: RGB arrays for visual stuff or numerical vectors for state-based environments. You’ll probably need to tweak the preprocessing in the wrapper functions for your specific observation format. The action space mapping might need changes too, depending on whether you’re using discrete or continuous actions. Most work happens in the environment wrapper layer, so the core agent stays the same. Just make sure your custom environment follows the standard gym interface and you can adapt it without major changes.
for sure! the starter agent can handle custom envs, but be ready for some compat issues. i had to tweak the observation preprocessing quite a bit since the default wrapper wants specific formats. also, double-check that your action space matches what the agent expects too.
Yes, the Universe starter agent works with custom environments, but you’ll need to handle the transition layer properly. The biggest challenge isn’t gym interface compatibility - it’s Universe’s async nature. The starter agent expects specific timing behaviors and state transitions that might not match your custom environment’s flow. Pay close attention to the remote environment wrapper and how it manages the connection protocol. If your custom environment runs locally, you’ll probably need to modify or bypass some networking components entirely. Just make sure your environment can handle the async observation requests the agent sends.
Yes, it can definitely work with custom environments, but be aware that there may be some issues. One key aspect is that reward scaling can become problematic with custom setups, as the starter agent is built with certain reward ranges in mind. If your rewards differ significantly, you may need to normalize or clip them. Additionally, pay close attention to how you handle episode termination; your done flag logic must align with the agent’s expectations, particularly if your environment lacks well-defined episode endings. Customizing the preprocessing pipeline for your observation space will likely be necessary.
The OpenAI Universe starter agent is indeed adaptable and can be modified to work with custom gym environments. It’s designed to handle various environments as long as they adhere to the standard gym API requirements. Specifically, your custom environment needs to implement reset(), step(), and render() methods, along with defining the necessary action and observation spaces. As long as your environment provides compatible observations, the agent should function properly. You may need to make some adjustments to the preprocessing pipeline if your observation dimensions or types differ significantly from those in the original examples.