36 ms·

From my initial reading, the end user can't create environments? Is that a feature that I can expect will eventually come?

by NamTaf 10y ago

lawless123 10y ago
Does that mean we can't train it on new games? only preexisting ones?
- NamTaf 10y ago
  If it's true, I believe we have to wait for the OpenAI team to build new gym environments before we can train in new games. I only briefly poked around because it's nearing on midnight here - maybe you can pull open the examples included and work out how to rewire them to work on new games, maybe not. Either way, I've got a particular use case I'd like to make a gym for so I'm interested in finding out.
mappingbabeljc 10y ago
You can create environments - it's coming! We'll be releasing many components over next few months.
- lawless123 10y ago
  Brilliant :)
pveierland 10y ago
It looks like the image from the server and control information to the server is sent through the VNC protocol. Other information such as the reward signal from the environment server is sent through a WebSockets protocol using JSON: https://github.com/openai/universe/blob/master/doc/protocols.rst https://github.com/openai/universe/blob/master/doc/protocols... You should be able to implement this protocol for your environment and run a VNC server for the rest. A new class for the client representing your environment can be based on this: https://github.com/openai/universe/blob/master/universe/envs/vnc_gtav.py https://github.com/openai/universe/blob/master/universe/envs... Then register the class with OpenAI Gym: https://github.com/openai/universe/blob/master/universe/__init__.py#L1495 https://github.com/openai/universe/blob/master/universe/__in... After creating the environment using gym.make you need to add information about your remote in the call to configure: env = gym.make('gtav.SaneDriving-v0') env.configure(remotes="vnc://localhost:vnc_port+rewarder_port") https://github.com/openai/gym/blob/master/gym/core.py#L234 https://github.com/openai/gym/blob/master/gym/core.py#L234 https://github.com/openai/universe/blob/master/universe/envs/vnc_env.py#L137 https://github.com/openai/universe/blob/master/universe/envs... This is only based on a cursory reading, but it should be possible to use custom environments with OpenAI Universe as it is today.

From my initial reading, the end user can't create environments? Is that a feature that I can expect will eventually come?

by NamTaf 10y ago

From my initial reading, the end user can't create environments? Is that a feature that I can expect will eventually come?

lawless123 10y ago

Does that mean we can't train it on new games? only preexisting ones?

NamTaf 10y ago
If it's true, I believe we have to wait for the OpenAI team to build new gym environments before we can train in new games. I only briefly poked around because it's nearing on midnight here - maybe you can pull open the examples included and work out how to rewire them to work on new games, maybe not. Either way, I've got a particular use case I'd like to make a gym for so I'm interested in finding out.

mappingbabeljc 10y ago

You can create environments - it's coming! We'll be releasing many components over next few months.

lawless123 10y ago
Brilliant :)

pveierland 10y ago

It looks like the image from the server and control information to the server is sent through the VNC protocol. Other information such as the reward signal from the environment server is sent through a WebSockets protocol using JSON: https://github.com/openai/universe/blob/master/doc/protocols.rst https://github.com/openai/universe/blob/master/doc/protocols... You should be able to implement this protocol for your environment and run a VNC server for the rest. A new class for the client representing your environment can be based on this: https://github.com/openai/universe/blob/master/universe/envs/vnc_gtav.py https://github.com/openai/universe/blob/master/universe/envs... Then register the class with OpenAI Gym: https://github.com/openai/universe/blob/master/universe/__init__.py#L1495 https://github.com/openai/universe/blob/master/universe/__in... After creating the environment using gym.make you need to add information about your remote in the call to configure: env = gym.make('gtav.SaneDriving-v0') env.configure(remotes="vnc://localhost:vnc_port+rewarder_port") https://github.com/openai/gym/blob/master/gym/core.py#L234 https://github.com/openai/gym/blob/master/gym/core.py#L234 https://github.com/openai/universe/blob/master/universe/envs/vnc_env.py#L137 https://github.com/openai/universe/blob/master/universe/envs... This is only based on a cursory reading, but it should be possible to use custom environments with OpenAI Universe as it is today.