Comments (3)
Hi @Ademord,
That line isn't the correct one. GymTask
is a wrapper class to provide OpenAI compatible APIs.
What you are looking for is in the tennis_maddpg.py:15-19.
Let me know if you want some help. Getting better Unity integration is on my long todo list but as you can see from this repo's activity... I'm quite busy with other stuff. But, people in need always add priority to things :)
from ai-traineree.
Hello, thanks for getting back at me, I managed to use the GymWrapper unity provides with SB3 so I am so far satisfied on what I need, but I might come back to this repo to contribute (after the thesis is done).
If you dont mind writing me a sentece to highlight the contribution of your repo in comparison to the GymWrapper implementation I can mention it in my thesis :)
from ai-traineree.
Cool :) Any contributions are more than welcome. Again, feel free to let me know, either here or on my email, whether there's anything you'd like to see/use.
As for the difference... So both packages, ai-traineree and ml-agents, are much more than just the Unity wrappers. Specifically on the difference between MultiAgentUnityTask and UnityToGymWrapper is that the former allows controlling many agents in an environment whereas the latter focuses only on a single agent. At least that was the last time I checked which was many months ago.
In case you are asking about the difference between two packages. In general I think both are trying to solve a similar problem, i.e. providing a package with customizable deep reinforcement learning agents. There is an overlap except that ml-agents is much bigger project as it has been around for longer and is a "job" for many people. The reason why AI Traineree exists is that 1) ml-agent didn't support multi agent, 2) they have super convoluted codebase which seems to be focused on agents usage, not development, and 3) I don't agree with their philosophy on training agents. (And lack of communication but, hey, I'm just a rando on the internet so why should they reply?) Their product is Unity and ml-agents is just an addition.
Don't feel the pressure but if you find this package useful and thought about citing it then feel free to use citation just added in to the Readme.
from ai-traineree.
Related Issues (16)
- Serialize buffers HOT 1
- Serialize networks
- Serialize agents
- AgentFactory: Rainbow agent creationg
- bugfix examples/multi_agent/prison_iql.py HOT 2
- PPO on MultiEnvRunner doesn't work properly
- Curiousity in PPO HOT 2
- Dummy agent HOT 1
- Unify epislon-greedy
- Unify input data
- 'int' object has no attribute 'to_feature' in DQN examples HOT 2
- Assertion failed for multi-agent environment HOT 2
- Bug: PPO has both method and property called `train`
- Test: Reloaded DDPG agent doesn't differ after data feeding
- What is the best way to reshape the observation before feedint it to the network? HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from ai-traineree.