open_spiel VS tensortrade

Hi, you can check out OpenSpiel: https://github.com/deepmind/open_spiel/

This includes single-agent Gymnasium wrappers for DM Control, DM Lab, Behavior Suite, Arcade Learning Environment, OpenAI Gym V21 & V26. Multi-agent PettingZoo wrappers support DM Control Soccer, OpenSpiel and Melting Pot. For more information, read the release notes here:

The RL agents in OpenSpiel (https://github.com/deepmind/open_spiel) are designed with this setting as the default (so like, DQN run in Tic-Tac-Toe would have two separate agents learning against each other: one knows how to play as player 1, the other as player 2).

I maintain a project called OpenSpiel, basically a library/suite of implementations of board games (mainly for AI research but can be used for whatever). It has a C/C++ core, but it also exposes the core API in Python, Rust, Go, and Julia: https://github.com/deepmind/open_spiel/ and a lot of AI algorithms in Python.

If you are looking for some programming game-theoretic algorithms, you can look at Gambit (http://www.gambit-project.org/) or OpenSpiel (https://github.com/deepmind/open_spiel). OpenSpiel has a Julia API too exposing the core and games, but does not have any of the basic game theoretic algorithms in Julia, so that would make a nice exercise (and maybe contribution to the project).

I looked at the paper before and thought some of the benchmarks in the paper were cherry picked and that the whole let’s write env in compiled languages things has been done a lot already and only works well for games / physics / environments that are extremely well defined . I usually end up back in ray env bc I usually need features like parametric action spaces, or predictive models built inside environments, or historical data. Codingwise I looked at the example file that they had and felt like it wasn’t for me bc I don’t like Bazel ( I use pants for python monorepo ) and the c++ api was overly verbose for me. I don’t feel like I could implement reward shaping or env business logic in a very clear way. I don’t do much c++ dev work though and someone who is a seasoned c++ dev might not care. I liked the openspiel c++ api for env a lot more. https://github.com/deepmind/open_spiel/blob/master/docs/developer_guide.md I have found that building a new env can sometimes be frustrating to debug and I would probably debug a python env before converting to an openspiel/envpool env if I had to use a C++ env for a new problem.

I 404'ed when I tried to access the source code?

https://github.com/deepmind/open_spiel/tree/master/open_%20s...

Someone needs to create a web front end for this -- I would love to play it.

There's an extra space in the URL to their code (at the end of the article). The correct URL is: https://github.com/deepmind/open_spiel/tree/master/open_spie...

This is a wild guess. I am fairly sure that internally Deepmind uses their own tool, OpenSpiel. The code is kind of dense because it does a lot, but probably most of the functionality that you are looking for is somewhere in there

Another one is https://github.com/tensortrade-org/tensortrade

Predict stock prices with TensorTrade

Looks like this is still being contributed to. It's a good start but lacks some parts.